AI-Powered Search implementation checklist

Ensure the indexing script can be re-run on the same dataset without creating duplicate entries by using deterministic IDs based on content hashes.

Implement a fixed chunking strategy (e.g., recursive character splitting) with a defined token overlap (e.g., 10-15%) to maintain context across boundaries.

Enforce a strict schema for metadata (dates, categories, user permissions) to ensure filtering logic does not fail during vector retrieval.

Configure a queue to capture documents that fail embedding generation or vector database insertion for manual review or automated retry.

Develop a mechanism to identify and index only new or modified documents rather than rebuilding the entire index on every update.

Implement RRF to combine and normalize scores from keyword-based (BM25) and semantic (Vector) search results.

Apply attribute-based filters (e.g., tenant_id, is_public) at the database level during the vector search to prevent data leakage.

Define a minimum cosine similarity or Euclidean distance floor to prevent displaying irrelevant results for queries outside the index domain.

Use an LLM to rewrite user queries into search-optimized terms or multiple variations to improve retrieval recall.

Strip conversational filler from user queries before generating embeddings to reduce noise in the vector space.

Integrate a reranking model (e.g., Cohere Rerank or BGE-Reranker) to process the top 20-50 retrieved candidates for final ordering.

Apply multiplicative boosts to search scores based on business metrics like document recency, popularity, or authority scores.

Implement an algorithm (e.g., Maximal Marginal Relevance) to ensure the top results are not redundant variations of the same content.

Verify that the embedding model and reranker support the specific languages present in the production dataset.

Assign higher weights to matches found in titles or headers compared to matches found in the document body.

Measure and document the 99th percentile response time for the full pipeline, including embedding, retrieval, and reranking.

Batch multiple text chunks into a single API call to the embedding provider to reduce network overhead during indexing.

Implement a cache (e.g., Redis) for the embeddings of the most frequent search queries to bypass LLM API latency.

Enable Product Quantization (PQ) or Scalar Quantization (SQ) in the vector DB to reduce memory usage and speed up search at scale.

Use connection pooling for the vector database to handle high concurrent search volumes without exhausting socket limits.

Curate a set of 50+ query-document pairs to calculate NDCG and MRR metrics after every major algorithm or index change.

Log all queries that return no results or results below the similarity threshold to identify content gaps.

Capture implicit (clicks) and explicit (thumbs up/down) feedback to create a training set for future model fine-tuning.

Monitor the distribution of query embeddings over time to detect if user behavior is shifting away from the indexed content distribution.

Implement distributed tracing (e.g., OpenTelemetry) to isolate latency bottlenecks between the embedding API and the vector DB.

Ensure every search result or AI summary includes clickable citations leading directly to the source document or chunk.

Show skeleton states for results immediately while the more expensive reranking or LLM summary steps complete.

Return and display the specific text snippets that triggered the match to provide immediate context to the user.

Fallback to standard keyword search if the embedding API or vector database becomes unavailable.

Test search result cards and filters on mobile viewports to ensure citations and metadata remain legible.