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Easy steps for an effective RAG pipeline with LLM models!
Easy steps for an effective RAG pipeline with LLM models!
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1. Document Embedding & Indexing
1. Document Embedding & Indexing
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We can start with the use of embedding models to vectorize documents, store them in vector databases (Elasticsearch, Pinecone, Weaviate) for efficient retrieval.
We can start with the use of embedding models to vectorize documents, store them in vector databases (Elasticsearch, Pinecone, Weaviate) for efficient retrieval.
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2. Smart Querying
2. Smart Querying
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Then we can generate query embeddings, retrieve top-K relevant chunks and can apply hybrid search if needed for better precision.
Then we can generate query embeddings, retrieve top-K relevant chunks and can apply hybrid search if needed for better precision.
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3. Context Management
3. Context Management
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We can concatenate retrieved chunks, optimize chunk order and keep within token limits to preserve response coherence.
We can concatenate retrieved chunks, optimize chunk order and keep within token limits to preserve response coherence.
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4. Prompt Engineering
4. Prompt Engineering
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Then we can instruct the LLM to leverage retrieved context, using clear instructions to prioritize the provided information.
Then we can instruct the LLM to leverage retrieved context, using clear instructions to prioritize the provided information.
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5. Post-Processing
5. Post-Processing
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Finally we can implement response verification, fact-checking and integrate feedback loops to refine the responses. 
Finally we can implement response verification, fact-checking and integrate feedback loops to refine the responses. 
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Happy to connect :) 
Happy to connect :) 
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 :) 
👀
John6666