Why Your AI Project Is Slow (And How to Fix It)

Your RAG pipeline takes 5 seconds to respond. That's too slow. Here are the usual culprits and how to fix them.

The Bottleneck Hierarchy

1. API latency (usually 1-3s) LLM API calls dominate end-to-end latency. Streaming helps — return first token in 500ms instead of waiting 3s for full response. Batch requests if you don't need real-time response.

2. Retrieval latency (usually 500ms-2s) Vector database queries, embedding generation, and network round-trips add up. Optimize: cache embeddings, tune vector DB indexing, use async requests.

3. Serialization and network (usually 100-500ms) JSON encoding/decoding, network hops, and middleware add overhead. Optimize: use binary protocols, reduce hops, cache intermediate results.

4. Orchestration overhead (usually 100-300ms) Chaining multiple steps (fetch documents, call model, format output) multiplies latency. Optimize: parallelize where possible, reduce steps, use streaming.

Quick Wins

1. Enable streaming responses from the LLM API
2. Make embedding and retrieval calls parallel, not sequential
3. Cache embeddings and vector search results
4. Reduce prompt size without losing critical context
5. Use smaller models for less complex tasks

Measuring Matters

Profile your actual pipeline with real requests. The bottleneck is usually different than expected. Optimize the bottleneck that actually takes the most time.

Most slow systems can be 2-3x faster with targeted optimization.