Structured Generation
AI and machine learning skill, available on Zeplik
Structured Generation is a ready-to-run AI and machine learning skill on Zeplik. Constrained and structured LLM output — Outlines, Guidance, Instructor (Pydantic), DSPy declarative programs, and prompt libraries. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
The Structured Generation skill loads automatically when your request matches it, or you can invoke it directly by typing /structured-generation in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.
What the Structured Generation skill can do
- Force LLM output to conform to a JSON or XML schema
- Extract structured data from text using Pydantic validation with retries
- Build declarative AI pipelines and auto-optimize prompts with DSPy
- Constrain generation with regex or grammars to guarantee valid formats
Try these prompts on Zeplik
Pick a prompt to open it in the Zeplik app. If you are not signed in yet, your prompt is waiting for you the moment you do.
How the Structured Generation skill works
/structured-generation
Umbrella for structured LLM generation. The user needs reliable, schema-conforming output; establish the target schema and framework, then deliver generation/extraction code with validation. For raw prompt wording route to prompt-engineering.
Dispatch table
Pick the reference file(s) that match the request, read them, then answer. Read at most 2-3 files per turn.
| Topic | Read |
|---|---|
| Build complex AI systems with declarative programming, optimize prompts automatically,… | references/dspy.md |
| Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, e… | references/guidance.md |
| Extract structured data from LLM responses with Pydantic validation, retry failed extra… | references/instructor.md |
| Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type… | references/outlines.md |
| Curated collection of high-quality prompts for various use cases. | references/prompt-library.md |
How to work
- Identify which leaf topic the request maps to from the dispatch table above; establish the concrete inputs (language, dataset, framework, file format) and the goal. Ask for a missing detail rather than guessing.
- Read the matching reference file(s) before answering. Read at most 2-3 per turn.
- Deliver runnable artifacts — code, configs, specs — with a short rationale, matching the user's existing conventions when they paste code.
- Confirm any decision the source flags (versions, thresholds, tradeoffs) with the user instead of guessing.
Usage
/structured-generation $ARGUMENTS
How to use the Structured Generation skill
Sign in to Zeplik
Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the Structured Generation skill right away.
Describe your AI and machine learning task
Ask in plain language, or type /structured-generation to invoke the skill directly. Zeplik recognizes the Structured Generation skill and applies its method.
Review and refine the result
Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.
Source and credit
- Author
- davila7 (D7 umbrella-consolidation)
- License
- MIT
Adapted from the open-source davila7/claude-code-templates project and tuned to run natively on Zeplik. View source on GitHub.
Frequently asked questions
- What is the Structured Generation skill?
- Structured Generation is a ready-to-run AI and machine learning skill on Zeplik. Constrained and structured LLM output — Outlines, Guidance, Instructor (Pydantic), DSPy declarative programs, and prompt libraries. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
- How do I use Structured Generation on Zeplik?
- Sign in to Zeplik and ask in plain language, or type /structured-generation in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
- Which AI model does the Structured Generation skill use?
- Any model you choose. Zeplik works across every model in one chat, so the Structured Generation skill runs on your preferred model for the task.
- Where does the Structured Generation skill come from?
- The Structured Generation skill is adapted from the open-source davila7/claude-code-templates project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
- How much does the Structured Generation skill cost?
- Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.
Related ai and machine learning skills
- Agent MemoryMemory and context management for LLM agents — short/long-term memory stores, conversation persistence, context-window strategies (summarization, trimming, retrieval) and prompt caching. Use for "give my agent memory / manage context"; for vector stores see vector-databases.
- AI Safety GuardrailsSafety and moderation for LLM apps — Constitutional AI, Llama Guard input/output moderation, and NeMo Guardrails runtime rails. Use for "add safety/moderation/guardrails to my LLM app"; for evaluating safety see llm-evaluation-harnesses.
- Autonomous Agent PatternsFramework-agnostic design patterns for autonomous coding agents — goal decomposition, planning, parallel/multi-agent orchestration and operational modes. Use for "how should I architect an autonomous agent"; for concrete frameworks see ai-agent-frameworks.
- Gemini CLIRun Gemini CLI (Gemini 3 Pro) for code/plan review and huge >200k-token context analysis in the terminal
- InterpretabilityMechanistic interpretability of neural networks — TransformerLens, NNsight remote access, sparse autoencoders (SAELens), and causal interventions (pyvene). Use for "probe/intervene on model internals" or "train an SAE"; for architecture basics see llm-architectures.
- LLM App PatternsProduction patterns for LLM applications — RAG architecture, embeddings, LLMOps, and end-to-end app design. Use for "architect a production LLM/RAG app"; for the vector store see vector-databases, for agents see ai-agent-frameworks.
More on Zeplik
Try Structured Generation on Zeplik
Every model, one chat. Bring the Structured Generation skill into your next conversation and let the assistant do the work.