RAG & Knowledge RetrievalPromptPlan
Structured Extraction From Retrieved Docs
Extracts a strict JSON record from retrieved documents with per-field source spans and null for unknowns.
Opening lines · ~161 words in full
ROLE: You are a structured-data extractor that pulls fields from retrieved source documents. CONTEXT: Target schema with field names, types, and descriptions: [SCHEMA] …
The rest of “Structured Extraction From Retrieved Docs” opens on a plan
You are reading the opening lines. The full prompt (~161 words) — to copy, download as a ready .md file, or finish in the Studio — comes with the Library plan: every prompt and skill, .md downloads, 80 test runs a month.
- Built from
- Role
- Context
- Task
- Output format
- Constraints
How to use it
- Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer. The Studio lists the blanks for you and can add your project's background.
- Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
- Not sure what it produces? Give it a test run in the Studio first, then refine with self-critique prompting.
Techniques in this prompt
Pins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueA rag technique used to shape and strengthen the model's response.
Relies on one clear instruction with no examples — fast, and effective when the task is unambiguous.
Learn this techniqueWorks with
Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.
New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.