Building Your First RAG Pipeline
The full pipeline, step by step
1. Chunk your documents. 2. Generate embeddings for each chunk. 3. Store in a vector database. 4. On a user query, embed the query. 5. Retrieve top-K similar chunks. 6. Inject them into the prompt as context. 7. Call the LLM for a final answer.
Prompting with retrieved context
Structure the final prompt clearly: "Using only the following context, answer the question. If the context doesn't contain the answer, say so." This instructs the model to ground its answer in retrieved facts rather than its general training.
Wiring it together with code
Using the Claude API path skills, write a Next.js route that: embeds the incoming query, queries your vector store, builds the augmented prompt, and calls Claude — this is a complete, working RAG endpoint.
Key Takeaways
- The full pipeline is: chunk → embed → store → query embed → retrieve → augment prompt → answer.
- Explicitly instruct the model to ground answers in the retrieved context.
- Instruct the model to admit when context doesn't contain the answer.
- This can be implemented as a Next.js API route using Claude API skills.
Build a working RAG endpoint
Build a Next.js route that embeds a query, retrieves from your vector store from Lesson 3, and calls Claude with the augmented prompt to answer.