Understand the mechanism

Fine-tuning updates weights from training examples, teaching response patterns without reliable record-level provenance. RAG indexes external documents, retrieves relevant passages at request time, and places them in context for generation. Updating RAG changes the index; updating a tuned model requires another training cycle. Read that as a pipeline, not magic. At each arrow, name the representation, owner, and possible loss.

A useful trace is input → preprocessing → model operation → postprocessing → action. Preprocessing may tokenize, parse, retrieve, resize, or filter. The model operation estimates a continuation, score, noise update, or preference. Postprocessing may validate a schema, fuse rankings, enforce policy, or attach provenance. Only then should the product act.

For a legal research assistant, record identifiers for every changeable stage. If two runs differ, you should be able to ask whether the input, prompt, model weights, retrieved corpus, decoding settings, tool result, or policy changed. Without those identifiers, randomness becomes the default explanation for every bug.

The mechanism guarantees only what its explicit deterministic stages guarantee. Learned components produce estimates based on training and current context. Prompting supplies instructions now, fine-tuning changes weighted behavior, RAG supplies external evidence now, and tools perform actions or deterministic lookups. Fine-tuning is not a database, and retrieval is not learning. Therefore a successful-looking output does not prove that the right evidence was used. Preserve intermediate artifacts when privacy permits: candidate lists, cited spans, memory IDs, judge scores, coordinates, or agent handoffs.

Latency and cost accumulate across the path. If stages take 120 ms, 480 ms, and 900 ms sequentially, the lower-bound latency is 1.5 seconds before network overhead. Parallel stages take approximately the slowest branch, but then require merging and timeout behavior. This arithmetic matters because an elegant pipeline that misses the user’s deadline is not operationally correct.

A policy assistant must answer from weekly regulations in a strict JSON schema. Prompt-only JSON validity is 86%; RAG improves factual freshness but not validity. A small format-tuning set raises validity to 99%, while RAG supplies dated clauses. Removing RAG makes current-fact accuracy collapse, proving each layer’s role. Notice the causal language: an observed input or configuration changed an intermediate artifact, which changed a measured outcome. “The model got worse” is not yet a diagnosis. A diagnosis points to a stage and offers a falsifiable test.

When drawing this mechanism, mark trust boundaries. External documents, user text, images, and agent messages are data, not governing instructions. Tools should receive typed arguments and least privilege. Stored traces and memories need access controls because observability can quietly become a second sensitive database.

1. Did the correct input reach preprocessing intact?
2. Was the intended model, prompt, index, or checkpoint loaded?
3. Which intermediate artifact first differs from a good run?
4. Did postprocessing reject, distort, or silently coerce the result?
5. Did the product action reflect the validated output?

Answer these in order. Jumping directly to prompt edits can mask a parser, permissions, retrieval, or serving defect.