GenAI and LLM interview questions

Query rewriting uses an LLM or rules to expand, correct, or split the user question into better search strings.

A reranker scores the first-stage hits more carefully, often with a cross-encoder.

HyDE generates a hypothetical answer, embeds that, and searches with the hypothetical embedding.

You generate several paraphrased queries, retrieve for each, and merge unique chunks.

Retrieval can miss the right chunk, return conflicting sources, or pack so much text that the model ignores the key sentence.

Re-embed changed chunks, upsert by stable IDs, and delete vectors for removed content.

You restrict candidates by tenant, date, product, or ACL before or during ANN search.

HNSW is a graph index with strong recall and simple incremental inserts, at higher memory cost.

Cosine cares about angle, L2 about Euclidean distance, inner product about both direction and magnitude.

Providing relevant passages and requiring citations reduces unsupported claims.

Citations let users and evals check claims against retrieved IDs.

Lower it for extraction, classification, SQL, and customer-support facts.

Temperature shapes the full distribution

Fine-tuned facts are frozen until the next training run.

Parameter-efficient fine-tuning trains small adapter matrices instead of all weights.

Further training on a narrow dataset can erase general skills the base model had.

Fine-tune for house style, tool schemas, and refusal policy

Loaders ingest PDFs, HTML, and databases into Document objects.

Buffer memory stores raw turns, summary memory compresses old history, and vector memory retrieves past facts by similarity.

LangChain Expression Language composes steps as runnable units with standard invoke, batch, and stream methods.