context-retrieval

To prevent AI hallucination, retrieve relevant context using embedding-based semantic search and optional BM25 hybridization to locate precise passages. This grounds AI responses by assembling a token-bounded context block with source metadata for verifiable answers.

The best way to combine semantic search and keyword signals is through hybrid retrieval. By merging embedding-based vector search with BM25 keyword signals, you maximize both recall and precision across technical docs and internal wikis.

Cross-encoder reranking improves vector search results by refining the top-k candidates retrieved via ANN vector stores. This process evaluates semantic relevance more deeply, ensuring the final assembled context block contains the most accurate excerpts.

Yes, you can use this retrieval approach for support ticket resolution and codebase questions. It applies to technical documentation, product FAQs, and internal knowledge bases, allowing developers to retrieve concise excerpts and cite exact file paths.

Yes, this context retrieval method provides source citations by verifying generated claims against retrieved chunks. It surfaces exact source paths, timestamps, and conflicting evidence alongside the ranked context block for transparency.

Token-limited context blocks restrict the total volume of retrieved passages fed into the model to avoid exceeding context windows. While this prevents token overflow, it requires accurate cross-encoder reranking to ensure critical information is not truncated.