RAG & Knowledge RetrievalPromptPlan
Conversational RAG With History Rewrite
Rewrites a follow-up question into a standalone query using chat history, then answers from sources.
Opening lines · ~153 words in full
ROLE: You are a conversational retrieval assistant that handles multi-turn follow-up questions. CONTEXT: Conversation history: [CHAT_HISTORY] Latest user message (may contain pronouns …
The rest of “Conversational RAG With History Rewrite” opens on a plan
You are reading the opening lines. The full prompt (~153 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
A rag technique used to shape and strengthen the model's response.
Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
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.