AI inference, explained through one question

Inference, rather than model training, accounts for most engineering work in deployed LLM systems and resembles familiar data-engineering tasks such as moving data, managing latency, caching, monitoring, and evaluating results. A documentation assistant typically combines a lightweight encoder that converts document fragments and questions into embeddings for semantic search with a larger decoder that generates answers from retrieved text, often using hybrid vector and keyword search to handle both meaning and exact technical identifiers. This retrieval-augmented generation approach keeps answers current, supports citations, and is generally more suitable for changing documentation than fine-tuning a decoder, which tends to improve style more reliably than factual recall and cannot directly cite sources. The discussion explains tokens, context limits, cosine similarity, embedding spaces, multilingual retrieval trade-offs, and the need to use the same encoder for documents and queries, while emphasizing that retrieval quality, source conflicts, reindexing, caching, and observability determine system reliability. It also covers low-temperature sampling for technical support, quantisation as a way to reduce a 7B model’s memory footprint from roughly 14 GB to about 4 GB while improving generation speed at some cost to complex reasoning, and performance measures such as time to first token, tokens per second, throughput, streaming, and p95 or p99 latency. Because retrieval does not eliminate hallucinations, the proposed system treats groundedness as a measurable property in which claims trace to retrieved sources and uses refusals when documentation lacks a relevant answer.

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