Safety Tune: build AI guardrails that actually stick

Safety Tune helps teams draft negative constraints so assistants avoid risky topics, stay on brand, and align with compliance expectations.

Negative constraints

Explicit do-not rules for safer model behavior.

Generate constraints like do not mention named competitors, do not provide financial advice, and do not disclose confidential customer details. Export the list into prompts, evaluations, and policy docs.

AI Guardrail Builder

Describe your scenario and risk focus. Safety Tune drafts a prioritized set of negative constraints you can paste into system prompts and safety tests.

Deployment scenario Primary risk themes to block

Strictness Audience

Idle

Generated negative constraints

Safety Tune generates draft guardrail language for review. You remain responsible for legal compliance, model testing, and organizational policy approval.

Frequently asked questions

What is a negative constraint in AI safety?

A negative constraint is a direct instruction that tells a model what it must not do, such as refusing to name competitors or avoiding medical diagnoses. These constraints reduce ambiguity during evaluation and help teams document intended prohibitions alongside positive task instructions.

Does Safety Tune replace legal or safety review?

No. Safety Tune accelerates drafting and helps standardize phrasing, but your organization must still review outputs for regulatory fit, jurisdictional requirements, and real world testing. Use generated constraints as a structured starting point within your governance process.

How should teams test constraints after generation?

Build prompt suites that include adversarial requests, benign edge cases, and multi turn conversations designed to elicit violations. Compare model behavior across versions, measure refusal quality, and refine wording when constraints are too broad or conflict with helpful task instructions.

Why Use Safety Tune: AI Guardrail Builder?

Speed

Safety Tune compresses hours of back and forth into minutes by turning your scenario and risk themes into a ready to review list of negative constraints. Instead of writing each prohibition from scratch, you get a coherent baseline you can edit, prioritize, and ship alongside your next prompt update. Faster drafting means your team can keep pace with model releases and policy changes without delaying launches unnecessarily.

Security

Clear negative constraints reduce accidental disclosure pathways by instructing models not to share secrets, credentials, or sensitive customer details. Safety Tune helps you phrase prohibitions explicitly so monitoring and incident reviews can recognize violations faster. Stronger guardrail language supports least privilege storytelling, where assistants help without exposing internal systems or private data.

Quality

Quality guardrails are specific enough to test and short enough to fit real prompts. Safety Tune emphasizes concrete do not statements aligned to your selected scenario, which reduces vague language that sounds strong but fails in evaluation. Better wording leads to fewer contradictory instructions and more consistent user experiences across channels.

SEO

When AI assists publishing workflows, unclear constraints can create misleading claims that damage trust and search quality. Safety Tune helps SEO and editorial teams encode do not invent statistics style rules directly into assistant behavior, reducing rework and protecting page accuracy. Consistent constraints support durable content governance that aligns with helpful content expectations.

Who Is This For?

Bloggers

Bloggers use Safety Tune to keep AI drafting assistants from making unverified claims or mentioning competitors in ways that create legal exposure. The generator turns your niche and risk notes into negative constraints you can embed into prompts for outlines, FAQs, and research summaries.

Developers

Developers rely on Safety Tune when shipping copilots and support bots where hallucinated APIs or unsafe instructions are unacceptable. The tool drafts constraint lines that pair well with retrieval and tool calling policies, so teams can iterate quickly while keeping guardrails explicit.

Digital marketers

Digital marketers use Safety Tune to prevent campaign assistants from promising guaranteed results or making comparative claims that require compliance review. Generated constraints help align creative automation with brand and regulatory standards across ads, landing pages, and localized variants.

What Safety Tune Is and Why Teams Rely on It

Safety Tune is a focused assistant for drafting negative constraints, which are explicit instructions that tell an AI model what it must not do during a conversation or task. Instead of hoping a general safety policy will cover every edge case, you write guardrails that match your product, your audience, and your regulatory environment. The tool helps you move from vague intentions such as be careful to enforceable language such as do not provide medical diagnoses or do not mention named competitors in comparative statements. That shift matters because modern language models can be helpful in many domains while still creating liability, brand, or user harm when they drift outside acceptable boundaries.

Negative constraints differ from positive instructions because they reduce ambiguity about failure modes. A positive instruction might ask the model to be professional, but professionalism can be interpreted in conflicting ways. A negative constraint states a prohibition with enough clarity that both humans and automated evaluators can recognize violations. Safety Tune is built to accelerate that drafting process so product managers, trust and safety teams, and developers can iterate quickly without starting from a blank page every time.

Why Negative Constraints Matter for Compliance and Trust

Organizations adopt AI features to improve speed and personalization, yet the same features can amplify reputational and legal risk if outputs are misleading, discriminatory, or outside licensed scope. Negative constraints are one of the most practical layers of defense because they attach directly to model behavior in plain language. They complement technical mitigations such as retrieval grounding and output filtering, and they help teams document what was intended when an incident review occurs.

From an operational perspective, constraints also improve consistency across channels. If your public chatbot and your internal copilot share similar policies, aligning their prohibitions reduces the chance that users receive contradictory guidance. Safety Tune supports that alignment by helping you generate a structured set of constraints you can reuse, version, and audit. When your policy changes, you can update the constraint list and propagate it through prompts, evaluation suites, and training materials for support staff.

Safety Tune is most powerful when it sits alongside model selection, retrieval design, tool permissions, and human escalation paths. Negative constraints help you express intent in language that can travel between teams, but they do not replace red teaming, content classifiers, or contractual commitments with vendors. Think of the generated list as a contract draft between your policy goals and your prompt engineering work, something you can diff over time as your product evolves.

How to Use Safety Tune Effectively in Real Workflows

Start by naming the environment where the model will operate, such as customer support, sales assistance, or internal research. Then list the topics that require the strongest boundaries, including regulated advice, competitor references, and personally identifiable information. Use the generator to produce a first draft of negative constraints, and treat that draft as a working document rather than a final policy. Next, prioritize the highest risk items based on your threat model and user research, and tighten language where ambiguity remains.

After you have a draft, pair it with test cases. For each constraint, create a small set of prompts designed to elicit violations, including adversarial phrasing and benign edge cases. Measure whether the model respects the constraint under your system prompt and tool configuration. Iterate by refining wording, splitting compound constraints, and removing contradictions between negative instructions and positive task instructions. Safety Tune is most effective when your team adopts a regular review cadence, especially after model upgrades or when new features change what users can ask.

Teams that get the best outcomes also connect constraints to analytics. When you standardize phrasing, you can tag refusals and near misses in a way that maps back to specific prohibitions. That feedback loop helps you identify whether a constraint is too vague, too strict, or misaligned with user needs. Safety Tune accelerates the first draft, while your operational data tells you what to change next.

Common Mistakes to Avoid When Building Guardrails

One frequent mistake is writing constraints that are too broad, which can cause the model to refuse helpful tasks or produce overly generic answers. Another mistake is stacking redundant prohibitions without prioritization, which increases prompt length and can confuse evaluation. Teams also sometimes forget to coordinate constraints with retrieval sources, leading to situations where the model is told not to mention a topic while your knowledge base still pushes content in that direction.

Another pitfall is treating constraints as a replacement for monitoring. Even well written prohibitions should be combined with logging, sampling, and escalation paths for sensitive categories. Finally, avoid copying constraints from another company without adaptation. Your risk profile, jurisdiction, and user expectations are unique, and Safety Tune works best when you tailor outputs to your specific context rather than relying on generic lists that sound authoritative but do not match your product reality.

Many guardrail failures trace back to documentation drift. The prompt in production does not match the prompt in the wiki, and nobody notices until an incident occurs. A practical habit is to store Safety Tune output in the same system where you version prompts, and to require a pull request style review when constraints change. Short descriptions of why a prohibition exists also help new teammates onboard without reversing careful decisions.

When you write those descriptions, focus on user impact and failure scenarios rather than abstract values. That specificity makes it easier to decide whether a constraint still applies after a feature launch. Safety Tune gives you a strong starting point for the constraint text itself, while your team supplies the institutional memory that keeps it relevant.

How It Works

Choose a scenario

Pick the deployment context so constraints match real user expectations.

List risk themes

Describe topics to block such as competitors, financial tips, or sensitive data.

Generate constraints

Safety Tune composes prioritized negative instructions aligned to strictness and audience.

Test and ship

Copy the output into prompts, run evaluations, and refine wording as needed.

About Safety Tune

Safety Tune builds practical tooling for teams that need AI features to be helpful without becoming a liability. We focus on negative constraints because they translate policy intent into testable behavior faster than vague guidance alone.

Our approach emphasizes collaboration between product, engineering, and compliance stakeholders. Safety Tune is designed to produce clear drafts you can debate, measure, and improve over time.

What Is Safety Tune and Why Every AI Product Team Needs It

Meta description: Learn what Safety Tune does for AI guardrails, why negative constraints matter, and how teams ship safer assistants without slowing innovation.

Estimated read time: 9 minutes

From Policy Documents to Executable Instructions

Most organizations already have policies that describe acceptable use of AI, but policies alone rarely translate cleanly into prompt language that a model can follow reliably. Safety Tune bridges that gap by helping teams produce negative constraints that are explicit enough to test, version, and embed into production systems. Instead of treating safety as a one time legal review, product teams can treat it as an ongoing engineering practice tied to releases, incidents, and user feedback loops.

Negative constraints are especially important because they reduce ambiguity about what failure looks like. When a model is told to be helpful, helpfulness can conflict with accuracy or privacy depending on the situation. When a model is told not to provide step by step instructions for harmful activities, evaluators have a clearer signal for compliance. Safety Tune accelerates drafting so teams spend less time debating phrasing and more time validating behavior with real prompts and realistic user journeys.

Why This Matters for Customer Facing Assistants

Customer facing assistants operate under scrutiny from users, competitors, and regulators. A single high visibility mistake can trigger support escalations, press coverage, and internal reviews that slow roadmap execution. Safety Tune helps teams prepare for that environment by generating constraint language that can be reviewed by legal and trust stakeholders early in the design process. The result is not guaranteed safety, but it is a more disciplined starting point than ad hoc rules added after launch.

Teams also benefit from consistency. If marketing and support use different chat experiences, mismatched guardrails can confuse customers and create uneven risk. A shared constraint library makes it easier to align tone, prohibited topics, and escalation messaging across channels. Safety Tune supports that alignment by producing structured outputs you can store in a repository and reuse across prompts.

How Teams Integrate Safety Tune Into Release Cadences

Effective teams connect guardrail updates to release notes and model changes. When you upgrade a foundation model or change tools the assistant can call, behaviors can shift in subtle ways. Safety Tune helps you refresh constraints in the same sprint cycle so documentation and evaluation suites stay synchronized. Teams often pair the output with automated tests that check for disallowed content categories and with manual review for nuanced cases involving professional advice or regional regulations.

Building a Culture of Practical Safety

Safety Tune is not a substitute for governance, but it supports a culture where safety work is concrete and iterative. Instead of treating safety as a gate at the end, teams can treat constraint drafting as a recurring design activity. That mindset tends to improve cross functional collaboration because prompts become shared artifacts that engineers, designers, and policy owners can discuss with specificity.

Return to the Safety Tune generator on Home and build your first constraint set today.

Safety Tune Versus Manual Alternatives: Which Saves More Time?

Meta description: Compare drafting AI guardrails manually with Safety Tune, and see where automation saves time without sacrificing policy quality.

Estimated read time: 10 minutes

The Hidden Cost of Starting From Scratch

Manual drafting can produce excellent constraints when experts invest significant time, but the cost shows up in meetings, revisions, and duplicated effort across teams. A policy owner might write strong language in a document, yet engineers still need prompt ready sentences that fit token budgets and integrate with evaluation harnesses. Translating policy into prompt language is skilled work, and it is easy to lose nuance when multiple people edit the same draft without a shared template.

Manual approaches also struggle with scale. As you add languages, locales, and product lines, the number of variations multiplies. Without tooling, teams re implement similar prohibitions repeatedly, increasing the risk of inconsistency. Safety Tune reduces that friction by generating a structured first pass that teams can edit quickly, rather than forcing every project to reinvent baseline guardrails.

Where Safety Tune Accelerates the Workflow

Safety Tune is strongest in the early drafting stage, when you need breadth and clarity fast. It helps you enumerate categories such as competitor mentions, financial advice, and disallowed medical claims, then phrase them as direct negative instructions. That output becomes a checklist for review and a foundation for test planning. The time savings appear in fewer blank page sessions and fewer rounds of basic wording fixes, freeing reviewers to focus on edge cases and jurisdictional nuance.

Automation also helps teams avoid inconsistent terminology. When different prompts use different words for the same prohibition, monitoring becomes harder. A generated baseline encourages standardized phrasing, which supports analytics and incident triage because teams can search for consistent strings across logs and prompts.

Where Human Review Remains Essential

No generator replaces legal advice or domain experts. Safety Tune does not know your regulatory obligations unless you reflect them in inputs and review. Human reviewers remain essential for interpreting local law, updating constraints after enforcement guidance, and validating behavior on adversarial prompts. The value of Safety Tune is that it moves expert time toward judgment and verification rather than repetitive sentence building.

A Practical Comparison for Busy Teams

If your team ships monthly, manual drafting may feel manageable until incidents spike or model upgrades change behavior unexpectedly. Safety Tune becomes a time saver when you need rapid iteration across multiple surfaces and when you want a repeatable method for updating constraints alongside engineering changes. The best results combine Safety Tune output with structured review, testing, and documentation practices that treat guardrails as living assets.

Go back to Home, open the generator, and compare a Safety Tune draft with your current manual baseline in under five minutes.

How to Use Safety Tune to Improve Your SEO in 2026

Meta description: Discover how clearer AI guardrails support trustworthy content workflows and stronger SEO outcomes in 2026.

Estimated read time: 11 minutes

Why Search Engines Reward Trustworthy Publishing Workflows

Search engines continue to emphasize helpful content created for people, not for manipulation. In 2026, that standard increasingly intersects with how teams use AI in content operations. When AI systems generate drafts, summaries, or on page modules, unclear guardrails can produce misleading claims, thin pages, or inconsistent entity information that undermines site quality. Safety Tune supports SEO indirectly by helping teams define what AI generated assistance must avoid, which reduces the chance that automation introduces harmful inaccuracies or off brand statements at scale.

Negative constraints also support editorial discipline. SEO teams often maintain detailed briefs, but those briefs must be reflected in the tools writers use. Safety Tune helps translate priorities such as avoid unverifiable statistics or do not imply guaranteed rankings into prompt level rules that assistants can follow. That alignment matters because search quality raters and users both respond poorly to pages that overpromise or contradict themselves.

Applying Safety Tune Across Content Types

For blogs and guides, constraints can prohibit medical certainty where writers are not qualified, or ban competitor comparisons unless reviewed. For programmatic pages, constraints can restrict automatically generated FAQs from inventing policy details. For localized content, constraints can prohibit translating legal claims without human verification. Each use case maps to a different risk profile, and Safety Tune helps teams draft a constraint set that matches the publishing workflow rather than relying on generic safety defaults.

SEO specialists should pair constraints with structured data governance. If your site uses schema markup, inconsistencies between visible text and structured fields can create trust issues. Safety Tune outputs can be referenced in editorial checklists so AI assisted drafts do not introduce conflicting product names, prices, or availability statements.

Measurement and Quality Signals

Improving SEO is not only about keywords. It is about durable quality signals such as user satisfaction, reduced bounce rates on key pages, and fewer corrections after publication. Clear guardrails reduce rework, which speeds up shipping without sacrificing standards. Teams can measure impact by tracking content revision counts, compliance flags, and user reported issues over time after constraints are tightened.

A 2026 Roadmap Mindset

In 2026, treat AI assistance as part of your content system. Update constraints when algorithms change, when your brand messaging evolves, and when regulatory guidance shifts. Safety Tune makes those updates easier to operationalize because you can regenerate baselines and diff them against prior versions. The SEO benefit emerges from consistency, accuracy, and trust, not from shortcut content.

Jump to the Home generator and draft constraints aligned with your 2026 content governance plan.

Top Five Use Cases for Safety Tune You Have Not Considered

Meta description: Explore uncommon but high value ways to deploy Safety Tune beyond basic chatbot safety.

Estimated read time: 10 minutes

Internal Copilots for Sensitive Employee Questions

Internal assistants often answer HR and IT questions where wrong guidance creates employee frustration or compliance exposure. Safety Tune can generate constraints that prohibit the model from interpreting company policy as legal advice, from sharing personal data about colleagues, or from confirming unverified organizational rumors. These constraints become part of an internal prompt package that reduces risk while still allowing useful routing to official documents and help desks.

Sales Enablement Tools That Must Avoid Overclaiming

Sales teams want fast answers about products and competitors, but overstated claims can create contractual and regulatory problems. Safety Tune helps draft prohibitions against guaranteed outcomes, unsubstantiated performance comparisons, and unauthorized pricing promises. The constraints can be paired with approved sources so representatives get helpful responses without drifting into risky territory during live calls or email drafts.

Education and Tutoring Assistants With Integrity Boundaries

Educational tools need constraints that prevent enabling academic dishonesty while still supporting learning. Safety Tune can help phrase prohibitions against completing graded assignments verbatim, supplying exam answers when the user requests stealth, or fabricating citations. The goal is not to block tutoring, but to steer assistance toward conceptual explanation and study strategies that institutions accept.

Developer Documentation Bots That Should Not Invent APIs

Documentation assistants can hallucinate function names or parameters, causing costly debugging. Safety Tune can generate constraints that instruct the model not to invent library APIs, not to present unverified code as tested, and to disclose uncertainty when context is incomplete. Combined with retrieval, these constraints reduce misleading technical guidance.

Incident Response Drafting Under Pressure

During incidents, teams need fast communication drafts, but premature certainty can worsen crises. Safety Tune can help define constraints that prohibit confirming root causes before investigation, prohibit sharing private customer details, and prohibit speculative blame. These guardrails support calmer, more accurate messaging while leadership coordinates facts.

Try these scenarios in the Home tool and generate constraint language tailored to each workflow.

Common Mistakes When Writing AI Guardrails and How Safety Tune Fixes Them

Meta description: Learn frequent guardrail errors teams make and how Safety Tune helps you draft clearer negative constraints.

Estimated read time: 10 minutes

Mistake One: Vague Prohibitions That Are Hard to Test

Teams often write constraints like do not be inappropriate, which is difficult to evaluate consistently. Safety Tune encourages concrete negative instructions tied to categories you select, such as competitor naming, regulated advice, or disallowed personal data handling. Concrete language makes automated tests meaningful and helps human reviewers spot gaps faster.

Mistake Two: Conflicting Instructions in the Same Prompt

A model may be told to answer every question fully while also being told never to discuss medical topics. Without careful design, those instructions collide. Safety Tune helps you draft constraints as a coherent set and prompts you to think about priority rules, such as refusing certain requests rather than partially answering them. Clear precedence reduces confusing outputs.

Mistake Three: Ignoring Locale and Audience Differences

A constraint that works for one market may be insufficient elsewhere. While Safety Tune does not replace local counsel, it helps you generate a baseline list you can localize and adapt. Teams that skip localization often discover too late that a prohibited category needs tighter wording for a specific language or regulatory regime.

Mistake Four: Failing to Update Guardrails After Model Changes

Model upgrades can change refusals, tone, and factual behavior. Guardrails written for an older model may no longer produce the same outcomes. Safety Tune supports iterative drafting so teams can regenerate and compare constraint sets as part of upgrade checklists. Treating constraints as versioned artifacts prevents silent drift.

Mistake Five: Neglecting User Experience When Refusing Requests

Hard refusals can frustrate users if messaging feels abrupt or unhelpful. Safety Tune focuses on what must not happen, but product teams should still design user facing refusal copy and alternatives such as directing users to authoritative resources. Combining constraint clarity with thoughtful UX keeps safety measures from damaging customer satisfaction.

Return Home to generate a clean constraint draft you can refine with your policy and UX standards.

Our Mission

Safety Tune exists to make responsible AI deployment more practical for teams that must move quickly without surrendering accountability. We believe safety work should be expressed in clear, testable language that engineers can implement and auditors can review. Our mission is to reduce the gap between high level policy intentions and the daily reality of prompts, evaluations, and user facing experiences.

We focus on negative constraints because they address failure modes directly. Organizations deserve tools that help them articulate prohibitions with precision, iterate after incidents, and maintain consistency across products. We are committed to building straightforward utilities that respect user time and prioritize transparency about what automated drafting can and cannot do.

We also recognize that trust is earned through reliability and communication. That is why we emphasize privacy conscious defaults, accessible design, and responsive support when users have questions about how to use Safety Tune responsibly in their environments.

What We Build

Safety Tune is a generator for negative constraints tailored to AI guardrail programs. It helps teams produce structured lists of do not style instructions aligned to scenarios such as customer support, marketing assistance, internal knowledge retrieval, and education. The output is meant to accelerate drafting and review, not to replace legal counsel or organizational policy ownership.

Our users include product managers, developer teams, trust and safety specialists, and SEO led content organizations that need consistent guardrails across many pages and prompts. We design for workflows where constraints are stored, versioned, and tested alongside code changes.

Our Values

Privacy

We treat user inputs as sensitive by default. Safety Tune is designed so teams can experiment with guardrail language without unnecessary data collection beyond what is required to operate the service. We aim to minimize retention, disclose practices clearly, and support user rights consistent with modern privacy expectations.

Speed

Iteration velocity matters when models and policies change frequently. We optimize the drafting experience so teams can generate a strong baseline quickly, compare alternatives, and move into testing without friction. Speed is never an excuse to skip review, but it removes bottlenecks that slow responsible shipping.

Quality

We prioritize outputs that are clear, readable, and adaptable. Quality means fewer ambiguous sentences that confuse evaluators and more consistent terminology that supports monitoring. We continually improve templates and guidance so generated constraints remain practical for real production prompts.

Accessibility

Guardrail tooling should be usable across devices and accessible to people who rely on assistive technologies. We use sufficient contrast, large touch targets, and structured HTML so teams can collaborate inclusively when reviewing constraints and navigating the site.

Our Commitment to Free Tools

We maintain Safety Tune as a free tool because foundational safety resources should be widely available, especially for small teams with limited budgets. Free access lowers the barrier to experimenting with structured guardrails and encourages adoption of better practices across the ecosystem. We may introduce optional premium features in the future, but our commitment is to keep core constraint generation helpful and reachable for independent creators and enterprises alike.

Contact and Feedback

We welcome feedback that helps us improve Safety Tune. If you have suggestions, bug reports, or questions about responsible use, email us at haithemhamtinee@gmail.com. We read messages carefully and prioritize improvements that strengthen clarity, safety, and user trust.

Privacy Policy

Last updated:

Introduction and Who We Are

This Privacy Policy explains how Safety Tune collects, uses, and shares information when you use our website and tools. We describe our practices in plain language and explain your rights under applicable privacy frameworks, including the General Data Protection Regulation for individuals in the European Economic Area where it applies.

We may update this policy from time to time to reflect changes in our services, legal requirements, or industry practices. When we make material changes, we will revise the last updated date shown on this page and, where appropriate, provide additional notice. Continued use of Safety Tune after changes become effective constitutes acceptance of the updated policy, except where your consent is required for certain processing activities.

This document also explains how we approach minimization, security safeguards, and international transfers where applicable. If you are accessing Safety Tune from outside your home country, your information may be processed in countries that provide a different level of protection, and we describe the safeguards we rely on in relevant sections below.

What Data We Collect

When you use Safety Tune, we may process information that you voluntarily submit into the tool interface, such as scenario descriptions and keywords used to generate negative constraints. We may also process technical data automatically, including IP address, device type, browser version, and standard server logs that help us maintain security and reliability.

We may use cookies and similar technologies to remember preferences, measure usage, and support advertising where enabled. Additional details appear in our Cookies Policy and in the cookies section below. We do not intentionally collect special categories of sensitive data through the tool, and you should avoid submitting information you are not permitted to share.

We may derive limited analytics metrics from usage events, such as aggregated counts of page views and feature interactions, to understand product performance. Where feasible, we aggregate or de identify such metrics so they are not readily associated with an identifiable individual.

How We Use Your Data

We use information to operate, maintain, and improve Safety Tune, including troubleshooting errors, analyzing aggregated usage trends, and preventing abuse. We may use inputs to generate outputs for you in real time and to improve product quality through internal testing, provided we apply appropriate safeguards against inappropriate retention where feasible.

We may communicate with you if you contact support, and we may send service related messages about security or policy changes. Where required, we rely on lawful bases such as legitimate interests in securing our services, performing a contract where applicable, or consent for certain non essential cookies and marketing technologies.

We do not sell your personal information in the traditional sense of exchanging data for money, and we do not use your content to train third party foundation models unless we explicitly disclose a different practice and provide a lawful basis for doing so.

Cookies and Tracking Technologies

We use cookies to enable essential site functionality, remember settings, and support analytics and advertising partners where configured. Essential cookies are necessary for basic operations such as load balancing, security protections, and preference storage. Analytics cookies help us understand aggregated traffic patterns. Advertising cookies may personalize ads and measure campaign performance.

You can control cookies through browser settings and through available consent tools on our site. Blocking certain cookies may impact functionality or your experience. For more detail, review our Cookies Policy, which includes a table describing common cookie categories and purposes.

Third Party Services

We may rely on third party services to operate Safety Tune, including Google Analytics for aggregated usage analytics and Google AdSense for advertising monetization where enabled. These providers may process certain information according to their own policies and configurations. We encourage you to review Google privacy disclosures for additional detail about how their technologies work.

When third parties process data on our behalf, we seek to select reputable providers and implement appropriate contractual and technical safeguards consistent with applicable law. Third party integrations may change over time, and we will update this policy to reflect material additions or replacements.

Your Rights Under GDPR

If GDPR applies to our processing of your personal data, you may have rights including access, rectification, erasure, restriction of processing, objection, and data portability in certain circumstances. You may also withdraw consent where processing is based on consent, without affecting the lawfulness of processing before withdrawal.

You may lodge a complaint with a supervisory authority in your country of residence if you believe our processing violates applicable law. To exercise your rights, contact us using the email address below. We may need to verify your identity before fulfilling requests and may explain limitations where legal exceptions apply.

Data Retention

We retain information only as long as necessary for the purposes described in this policy, including legal, accounting, and security requirements. Server logs and analytics data may be retained for shorter or longer periods depending on system configuration and operational needs. Support emails may be retained to resolve issues and maintain service history unless deletion is requested and legally permissible.

When data is no longer needed, we take steps to delete or de identify it in accordance with our internal retention procedures, subject to backup systems that may retain data for limited periods before automatic overwrite.

Children's Privacy

Safety Tune is not directed to children under thirteen, and we do not knowingly collect personal information from children under thirteen. If you believe a child has provided us with personal information, contact us and we will take appropriate steps to delete such information where required.

Changes to This Policy

We may modify this Privacy Policy to reflect changes in our practices or legal requirements. We will update the last updated date and encourage you to review this page periodically. Material changes may be communicated through additional notices where appropriate.

Contact Us

For privacy questions or requests, contact haithemhamtinee@gmail.com.

Terms of Service

Last updated:

Acceptance of Terms

By accessing or using Safety Tune, you agree to these Terms of Service. If you do not agree, do not use the service. We may update these terms periodically, and continued use after changes become effective constitutes acceptance of the revised terms except where additional consent is required.

Description of Service

Safety Tune provides a web based tool that helps users draft negative constraints for AI guardrails. Outputs are generated automatically and are provided for informational and productivity purposes. You are responsible for reviewing outputs and ensuring they meet your legal, contractual, and safety requirements before use in production systems.

Permitted Use and Restrictions

You agree to use Safety Tune only for lawful purposes and not to misuse the service, including attempting to disrupt, scrape, or reverse engineer the site in ways that violate applicable law or our security protections. You must not submit illegal content or use the tool to generate instructions intended to cause harm, harassment, or deception.

Intellectual Property

The Safety Tune website, branding, and associated materials are protected by intellectual property laws. Subject to these terms, we grant you a limited, non exclusive, non transferable license to use the service for its intended purpose. You may not copy, modify, or distribute our materials except as permitted by law or with our written consent.

Disclaimers and No Warranties

The service is provided as is and as available without warranties of any kind, express or implied, to the fullest extent permitted by law. We do not warrant that outputs will be accurate, complete, or suitable for any particular purpose. You assume all risk associated with reliance on generated content.

Limitation of Liability

To the fullest extent permitted by law, Safety Tune and its operators will not be liable for any indirect, incidental, special, consequential, or punitive damages, or for loss of profits, data, or goodwill, arising from your use of the service. Our total liability for any claim arising out of these terms or the service will not exceed the greater of zero dollars or the amount you paid us for the service in the twelve months before the claim, if any.

Cookie Notice and GDPR Compliance

We use cookies and similar technologies as described in our Cookies Policy and Privacy Policy. Where GDPR applies, we process personal data in accordance with applicable principles and support exercise of rights as described in our Privacy Policy.

Links to Third Party Sites

The service may reference or link to third party websites. We are not responsible for third party content, practices, or policies. Your use of third party services is at your own risk and subject to their terms.

Modifications to the Service

We may modify, suspend, or discontinue features of Safety Tune at any time, including for maintenance, security, or business reasons. We are not liable for any impact such changes may have on your workflows, though we aim to communicate material changes when practical.

Governing Law

These terms are governed by applicable law without regard to conflict of law principles, except where prohibited. Courts in the relevant jurisdiction may have exclusive venue, subject to mandatory consumer protections that cannot be waived.

Contact

Questions about these terms: haithemhamtinee@gmail.com.

Cookies Policy

Last updated:

What Are Cookies

Cookies are small text files stored on your device when you visit websites. They help websites remember preferences, maintain sessions, measure performance, and support advertising. This Cookies Policy explains how Safety Tune uses cookies and how you can control them.

How We Use Cookies

We use cookies to provide essential functionality, analyze traffic, and support advertising where enabled. Essential cookies are required for core site operations. Analytics cookies help us understand how visitors use Safety Tune so we can improve performance and usability. Advertising cookies may personalize ads and measure effectiveness.

Types of Cookies We Use

Cookie Name	Type	Purpose	Duration
st_session	Essential	Maintains basic session stability and security protections.	Session
st_prefs	Essential	Stores UI preferences such as reduced motion or consent state when enabled.	12 months
_ga	Analytics (Google Analytics)	Distinguishes users and helps measure site usage in aggregate.	Up to 24 months depending on configuration
_gid	Analytics (Google Analytics)	Stores a short lived identifier for session level analytics.	24 hours
ads_prefs	Advertising (Google AdSense)	Supports ad personalization and frequency management where ads are displayed.	Up to 24 months depending on configuration

Third Party Cookies

Third parties such as Google may set cookies when you use our site, including analytics and advertising technologies. These third parties process information according to their policies and your settings. We encourage you to review their disclosures for additional detail.

How to Control Cookies

You can manage cookies through browser controls and consent tools. Below are general instructions for major browsers.

Google Chrome

Open Chrome settings, select Privacy and security, then Cookies and other site data. You can block third party cookies, clear browsing data, and manage site specific permissions.

Mozilla Firefox

Open Firefox settings, select Privacy and Security, and adjust Enhanced Tracking Protection and cookie settings. You can clear cookies and manage exceptions for individual sites.

Apple Safari

On macOS, open Safari settings, choose Privacy, and manage cookies and website data. On iOS, use Settings, Safari, and adjust privacy and tracking options.

Microsoft Edge

Open Edge settings, select Cookies and site permissions, and configure tracking prevention and cookie controls. You can clear browsing data from the privacy menu.

Cookie Consent

Where required, we present a cookie consent mechanism that allows you to accept or reject non essential cookies according to applicable law. Your choices may be stored so we do not repeatedly ask, and you can change preferences through available controls or browser settings.

Contact

Cookie questions: haithemhamtinee@gmail.com.

Safety Tune: build AI guardrails that actually stick

AI Guardrail Builder

Frequently asked questions

Why Use Safety Tune: AI Guardrail Builder?

Speed

Security

Quality

SEO

Who Is This For?

Bloggers

Developers

Digital marketers

What Safety Tune Is and Why Teams Rely on It

Why Negative Constraints Matter for Compliance and Trust

How to Use Safety Tune Effectively in Real Workflows

Common Mistakes to Avoid When Building Guardrails

How It Works

Choose a scenario

List risk themes

Generate constraints

Test and ship

About Safety Tune

What Is Safety Tune and Why Every AI Product Team Needs It

From Policy Documents to Executable Instructions

Why This Matters for Customer Facing Assistants

How Teams Integrate Safety Tune Into Release Cadences

Building a Culture of Practical Safety

Safety Tune Versus Manual Alternatives: Which Saves More Time?

The Hidden Cost of Starting From Scratch

Where Safety Tune Accelerates the Workflow

Where Human Review Remains Essential

A Practical Comparison for Busy Teams

How to Use Safety Tune to Improve Your SEO in 2026

Why Search Engines Reward Trustworthy Publishing Workflows

Applying Safety Tune Across Content Types

Measurement and Quality Signals

A 2026 Roadmap Mindset

Top Five Use Cases for Safety Tune You Have Not Considered

Internal Copilots for Sensitive Employee Questions

Sales Enablement Tools That Must Avoid Overclaiming

Education and Tutoring Assistants With Integrity Boundaries

Developer Documentation Bots That Should Not Invent APIs

Incident Response Drafting Under Pressure

Common Mistakes When Writing AI Guardrails and How Safety Tune Fixes Them

Mistake One: Vague Prohibitions That Are Hard to Test

Mistake Two: Conflicting Instructions in the Same Prompt

Mistake Three: Ignoring Locale and Audience Differences

Mistake Four: Failing to Update Guardrails After Model Changes

Mistake Five: Neglecting User Experience When Refusing Requests

Our Mission

What We Build

Our Values

Privacy

Speed

Quality

Accessibility

Our Commitment to Free Tools

Contact and Feedback

Support email

What to include in your message

Business inquiries versus support requests

Privacy when you contact us

Privacy Policy

Introduction and Who We Are

What Data We Collect

How We Use Your Data

Cookies and Tracking Technologies

Third Party Services

Your Rights Under GDPR

Data Retention

Children's Privacy

Changes to This Policy

Contact Us

Terms of Service

Acceptance of Terms

Description of Service

Permitted Use and Restrictions

Intellectual Property

Disclaimers and No Warranties

Limitation of Liability