AI app for it and development · no coding needed
Prompt lifecycle and LLM evaluation workbench
Reduce prompt release risk while keeping evaluation evidence and production traces in one owned workspace.
Made for: AI engineering teams building and operating LLM applications
What it does for you
The problem
Prompt changes, evaluation runs, trace debugging and production monitoring live in separate rented tools, so teams lose version history, review evidence and cost visibility across the lifecycle.
What it gives you
Reviewer-approved prompt releases linked to evaluation evidence
What you give it
Prompt versionstest setsevaluator definitionstracesproduction metricsmodel configurations
Build your own version of Athina, Langtail Public Beta and more
One app with what these 7 AI tools do, yours to keep and change: Athina, Langtail Public Beta, Agenta, Freeplay, PingPrompt, Langfuse Prompt Experiments, Athina AI.
Everything these tools do, in one app
- Prompt management workspace Store, organize, and edit prompts in a central place.Found in Agenta, PingPrompt, Langfuse Prompt Experiments
- Version control Track prompt changes with history and roll back when needed.Found in Agenta, PingPrompt, Langfuse Prompt Experiments
- Visual diffs Compare prompt versions side-by-side to see changes clearly.Found in PingPrompt
- Inline copilot Make precise, text-level edits to prompts without rewriting everything.Found in PingPrompt
- Multi-LLM playground Test prompts across different models and parameters to compare outputs.Found in PingPrompt
- Concurrent prompt testing Run multiple prompt variations and models on large datasets at the same time.Found in Langfuse Prompt Experiments
- Evaluation framework Create test sets, run evaluators, and compare results to assess prompt quality.Found in Agenta, Langfuse Prompt Experiments
- Automated live evaluations Use LLM-as-a-judge to automatically assess output quality and detect hallucinations.Found in Langfuse Prompt Experiments
- Trace debugging Debug and trace queries and responses to understand model behavior.Found in Agenta, Langfuse Prompt Experiments, Athina AI
- Production monitoring Monitor live LLM applications for errors, feedback, and cost metrics.Found in Agenta, Athina AI
- Error detection Detect hallucinations, misinformation, and quality issues in AI outputs.Found in Athina AI
- Analytics dashboards View metrics like cost, latency, and quality to make informed decisions.Found in Langfuse Prompt Experiments
- Role-based access control Manage user permissions and governance for prompt changes.Found in Agenta
- Deploy without code changes Update prompts in production without editing application code.Found in Agenta
- Real-time collaboration Work together on projects with team members in real time.Found in Athina, Langtail Public Beta, Freeplay
- Integrations Connect with popular data sources, productivity tools, or content management systems.Found in Athina, Langtail Public Beta, Freeplay
- Natural language query input Ask questions or give commands in plain language.Found in Athina
- Automated data analysis Analyze data automatically with customizable reporting templates.Found in Athina
- Content generation Generate content tailored for different languages and dialects.Found in Langtail Public Beta
- Real-time translation Translate text in real time with contextual accuracy.Found in Langtail Public Beta
- Text analysis Analyze text for tone, sentiment, and readability.Found in Langtail Public Beta
- Interactive content creation Create interactive content using customizable templates.Found in Freeplay
- Automated suggestions Get automated suggestions to enhance content quality.Found in Freeplay
- Multi-media support Work with text, images, and video formats.Found in Freeplay
- Secure cloud storage Store data securely in the cloud with privacy and compliance.Found in Athina
How it works, step by step
- Store, organize and edit prompts in a central workspace
- Track prompt changes with history and roll back to a prior version
- Compare prompt versions side by side with visual diffs
- Make text-level prompt edits with an inline copilot
- Test prompts across models and parameters in a multi-LLM playground
- Run multiple prompt variations and models on large datasets concurrently
- Create test sets, run evaluators and compare results
- Run automated live evaluations with LLM-as-a-judge and hallucination checks
- Debug and trace queries and responses
- Monitor live applications for errors, feedback and cost
- Detect hallucinations, misinformation and quality issues in outputs
- View cost, latency and quality dashboards
- Apply role-based access control to prompt changes
- Update production prompts without code changes
- Support real-time collaboration on projects
- Connect data sources, productivity tools and content systems
- Accept natural language queries and commands
- Run automated data analysis with reporting templates
- Generate multilingual content variants
- Translate text in real time with contextual accuracy
- Analyze text for tone, sentiment and readability
- Build interactive content from customizable templates
- Surface automated suggestions to improve content quality
- Handle text, image and video inputs
- Store data in secure cloud storage with privacy and compliance controls
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Prompt lifecycle and LLM evaluation workbench with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Prompt lifecycle and LLM evaluation workbench with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data200 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce prompt release risk while keeping evaluation evidence and production traces in one owned workspace. For AI engineering teams building and operating LLM applications, convert prompt versions, test sets, evaluator results, traces and production metrics into reviewer-approved prompt releases linked to evaluation evidence. The benefit is a testable hypothesis, measured through accepted prompt releases per engineering hour and regressions after release; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect prompt versions, test sets, evaluator definitions, traces and production metrics, then follow this sequence: 1. Store, organize and edit prompts in a central workspace. 2. Track prompt changes with history and roll back to a prior version. 3. Compare prompt versions side by side with visual diffs. Resolve uncertain cases with qualified reviewers, approve reviewer-approved prompt releases linked to evaluation evidence, and measure accepted prompt releases per engineering hour and regressions after release against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules, including LLM-as-a-judge evaluation and hallucination detection. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final release decisions and production changes remain with the engineering owner. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, prompt ownership and usage permissions. Engineering owners approve substantive prompt changes and production scope. One application, one model provider and a bounded test set; final release decisions remain with the engineering owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One application, one model provider and a bounded test set; final release decisions remain with the engineering owner. Implement one approved input format, a bounded representative case set and the first two task modules: store, organize and edit prompts in a central workspace; track prompt changes with history and roll back to a prior version. Support the third module with operator review: compare prompt versions side by side with visual diffs. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Customer-owned prompt repositories, model provider APIs, trace stores and production application endpoints. Cloud storage, data sources, productivity tools and content management systems. Start with file exchange and validate destination specifications before promising direct deployment. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Prompt workspace and version history, Evaluation and trace review, Production monitoring and release. Use a project list for applications, a large central prompt editor with side-by-side version diff, and a right-hand panel for test sets, evaluators, traces and comments. Let users compare model outputs side by side. Display draft, in review, approved and rolled back states. Provide a reviewer queue with evidence anchored to the relevant prompt version and evaluation run. Make the task-specific outcome reviewer-approved prompt releases linked to evaluation evidence visible beside its evidence, review state and value baseline.
