Practical guidance for using embeddings reliably in real applications — most embedding-related production issues trace back to skipping one of these basics, not a fundamental limitation of the technique.
Consistency
- Use the same embedding model for everything you'll compare — mixing models breaks similarity comparisons (see Embedding Models)
- Re-embed all existing content if you ever switch models — old and new vectors aren't compatible
Text Preparation Before Embedding
# Consider what you're actually embedding — raw, unprocessed
# text isn't always the best input
Bad: embedding an entire 50-page document as one vector
→ averages out meaning across too much content, losing
specificity
Better: chunk the document into focused sections first (see
Document Chunking), embed each chunk separatelyWhat text you choose to embed — and at what granularity — meaningfully affects retrieval quality, a core consideration explored further in Chunking Strategies.
Storage & Scale Planning
- For small collections (thousands of items), brute-force comparison is fine — don't over-engineer with a vector database prematurely
- For large collections, plan for a real vector database with efficient indexing from the start, rather than retrofitting later
- Consider dimensionality tradeoffs at genuine scale (see Embedding Dimensions)
Evaluation, Not Assumption
"It returns plausible-looking results in a few manual tests" isn't the same as "retrieval quality is actually good." Build a small evaluation set of real queries with known good/relevant answers, and measure retrieval performance against it — the same discipline used for prompt evaluation, applied to retrieval.
Cost Awareness
Embedding costs are generally low per call, but re-embedding large collections repeatedly (e.g. on every content update, without checking whether the content actually changed) adds up at scale — embed once, cache/store the result, and only re-embed when content genuinely changes.
Common Mistakes
- Embedding overly large chunks of text, diluting the specific meaning a query might be looking for
- Never evaluating retrieval quality with real test queries, only trusting that "it seems to work"
- Re-embedding unchanged content unnecessarily, wasting cost and compute
Interview Relevance
"What would you check first if a semantic search feature is returning poor results?" — embedding model consistency, chunk size/granularity, and whether retrieval has actually been evaluated against real queries are the practical first places to look.
Practice Question
A newly launched semantic search feature returns oddly unrelated results for many queries. List three things you'd check, in order of how likely/easy they are to be the culprit.
