Kindly Walk me through your chunking strategy, embedding choices, vector index design, and how you balance latency with cost
This question came up during a system design round for a Lead AI Engineer role. The interviewers weren’t just looking for a basic LangChain tutorial answer; they wanted to see how I handle real-world scale. At 10 million documents, you’re dealing with 50 to 100 million text chunks. The core challenge they were probing was the trade-off between high retrieval accuracy (which usually costs more in compute and memory) and strict latency/cost constraints in a production environment. Here is how I structured my answer to address their concerns and pass the round.
I started by breaking my architecture down into five core components, explaining my thought process for each:
1. Data Ingestion & Chunking Strategy
- The Baseline: I explained that fixed-size chunking (512–1024 tokens) with a 10–20% overlap is the standard to preserve context across boundaries.
- Handling Messy Data: For 10M docs, many will be messy PDFs. I proposed using semantic chunking (splitting at paragraph/section boundaries) for structured data rather than arbitrary token cuts.
- Hierarchical Indexing: To give the LLM better context, I suggested a parent-child approach: embed the small “child” chunks for precise retrieval, but pass the larger “parent” chunk to the LLM to generate the answer.
- Scale & Dedup: I noted that 10M docs will yield ~50M–100M chunks. I’d use deterministic chunk IDs (hashing normalized text) to avoid paying to re-embed duplicate content.
2. Embedding Model Choice
- Cost vs. Performance: At 100M chunks, API-based embedding costs would bleed the budget. I recommended self-hosting open-source models like
bge-large-en-v1.5ornomic-embed-texton dedicated GPUs using vLLM or TEI. - Dimensionality: I brought up Matryoshka Representation Learning (MRL). By using MRL models, we can truncate embeddings (e.g., from 1024 to 256 dimensions) to save massive amounts of vector storage and speed up search with minimal accuracy loss.
3. Vector Index Design & Database
- Database Selection: For 50M+ vectors, I advised against basic
pgvector. I recommended distributed, purpose-built vector databases like Milvus, Qdrant, or Weaviate that handle horizontal sharding natively. - Index Algorithm: I recommended HNSW (Hierarchical Navigable Small World) for the best balance of high recall and low latency.
- Metadata Sharding: I emphasized partitioning the index using metadata (like
tenant_idordoc_category) to restrict the search space before the vector search even begins, drastically improving query speed.
4. Retrieval Latency Optimization
- Target: I set a strict goal of < 200ms for the retrieval phase.
- Hybrid Search: To handle exact-match queries (like product codes or specific names) that dense vectors often miss, I proposed combining dense vector search with sparse keyword search (BM25) using Reciprocal Rank Fusion (RRF).
- Caching: I suggested implementing a semantic cache (using Redis or GPTCache) for frequent user queries to bypass the embedding and vector search steps entirely.
5. Cost & Latency Trade-offs (The Climax) Finally, I tied it all together by addressing the core trade-off they asked about:
- Memory vs. Disk: HNSW indexes are incredibly fast but live in RAM, which is expensive. If cloud memory costs were a strict blocker, I told them I would switch to IVF-PQ (Inverted File with Product Quantization) or disk-based indexes like DiskANN. This trades a slight drop in recall for massive memory savings.
- Reranking: To ensure high-quality answers without blowing up LLM context-window token costs, I suggested retrieving the top 20 chunks and passing them through a lightweight Cross-Encoder Reranker (like
bge-reranker) to select the final top 5 for the LLM. This adds ~50ms of latency but drastically reduces hallucinations.
By structuring my answer this way, I showed the interviewers that I could balance cutting-edge AI techniques with strict production realities.