Post
656
Easy steps for an effective RAG pipeline with LLM models!
1. Document Embedding & Indexing
We can start with the use of embedding models to vectorize documents, store them in vector databases (Elasticsearch, Pinecone, Weaviate) for efficient retrieval.
2. Smart Querying
Then we can generate query embeddings, retrieve top-K relevant chunks and can apply hybrid search if needed for better precision.
3. Context Management
We can concatenate retrieved chunks, optimize chunk order and keep within token limits to preserve response coherence.
4. Prompt Engineering
Then we can instruct the LLM to leverage retrieved context, using clear instructions to prioritize the provided information.
5. Post-Processing
Finally we can implement response verification, fact-checking and integrate feedback loops to refine the responses.
Happy to connect :)
1. Document Embedding & Indexing
We can start with the use of embedding models to vectorize documents, store them in vector databases (Elasticsearch, Pinecone, Weaviate) for efficient retrieval.
2. Smart Querying
Then we can generate query embeddings, retrieve top-K relevant chunks and can apply hybrid search if needed for better precision.
3. Context Management
We can concatenate retrieved chunks, optimize chunk order and keep within token limits to preserve response coherence.
4. Prompt Engineering
Then we can instruct the LLM to leverage retrieved context, using clear instructions to prioritize the provided information.
5. Post-Processing
Finally we can implement response verification, fact-checking and integrate feedback loops to refine the responses.
Happy to connect :)