RAG (Retrieval-Augmented Generation) implementation check...

Implement recursive character splitting or markdown-aware chunking to ensure paragraphs and code blocks stay intact rather than using fixed-size character limits.

Set a 10-15% overlap between consecutive chunks to maintain semantic context and ensure entities split across boundaries are still retrievable.

Attach source URLs, document IDs, and page numbers to every chunk to enable downstream source attribution and filtering.

Implement a mechanism to track document hashes and delete or update stale embeddings in the vector database when source files change.

Strip HTML tags, excessive whitespace, and non-printable characters from raw text before embedding to reduce noise and token usage.

Verify that the vector database distance metric (Cosine, Dot Product, or Euclidean) matches the specific metric used to train the embedding model.

Adjust ef_construction and M parameters in HNSW indexes to balance indexing speed against search recall based on your dataset size.

Implement batching for embedding API calls to maximize throughput and reduce network overhead during the initial data load.

Configure automated snapshots or persistence for the vector store to prevent data loss during container restarts or service failures.

Implement metadata filtering or separate collections to ensure users can only retrieve data they have explicit permissions to access.

Combine vector search with keyword-based BM25 search to improve retrieval for specific terminology, acronyms, or product IDs.

Apply a Cross-Encoder reranker to the top-k (e.g., top 20) results to re-order the most relevant context to the top for the LLM.

Establish a minimum similarity score cut-off to prevent the LLM from receiving irrelevant noise when no high-quality matches exist.

Use an LLM step to transform conversational user queries into standalone search terms, removing pronouns and context-dependent references.

Set a hard timeout for retrieval and reranking steps to ensure the total response time stays within acceptable P99 limits.

Calculate and enforce a maximum token limit for retrieved context to avoid exceeding the LLM's context window or incurring excessive costs.

Explicitly instruct the model in the system prompt to only use the provided context and to state 'I do not know' if the answer is missing.

Prompt the LLM to provide inline citations (e.g., [Source 1]) matching the metadata attached to the retrieved chunks.

Implement Server-Sent Events (SSE) or WebSockets to stream the LLM response to the client, reducing perceived latency for the user.

Integrate a secondary check or NLI (Natural Language Inference) model to verify that the generated answer is logically supported by the context.

Measure how often the correct document appears in the top-k results using a golden dataset of query-document pairs.

Capture 'thumbs up/down' feedback in the UI and log it alongside the query, retrieved context, and generated response for future fine-tuning.

Log input and output tokens per request to monitor costs and identify anomalous usage patterns or prompt injection attempts.

Deploy tools like RAGAS or TruLens to automatically score faithfulness, relevance, and answer correctness in a staging environment.

Set up alerts for high error rates from embedding or LLM providers (429, 500, or 503 status codes).