Understand the mechanism

During training, examples pass forward through the network, a loss compares predictions with targets, gradients flow backward, and an optimizer updates weights. During inference, only the forward computation is needed, often with autoregressive token generation and a KV cache. Fine-tuning is additional training; RAG and prompting are inference-time techniques.

Trace causality rather than memorizing vocabulary. First identify the state that exists before the operation. Next identify the computation and anything it persists. Finally identify what reaches the caller and what remains uncertain. That separation prevents a common category error: treating a convenient interface as proof that the underlying system learned, retrieved, secured, or validated something.

Here is the compact calculation to anchor the mechanism: one epoch over 80,000 examples with batch size 100 requires 800 optimizer steps; five epochs require 4,000 steps. The equation is useful only with its assumptions. Ask which quantities were measured, which were estimated, and whether an average hides a tail or subgroup. If the mechanism cannot explain a surprising metric, inspect the boundary conditions before tuning randomly.

A classifier trains for five epochs on 80,000 labeled tickets and freezes a checkpoint. Every new ticket then runs a forward pass in milliseconds. Adding today’s policy to a prompt helps that request but does not alter the checkpoint.

The worked number is one epoch over 80,000 examples with batch size 100 requires 800 optimizer steps; five epochs require 4,000 steps. State the unit and denominator whenever you report it. A percentage without a denominator can conceal a tiny sample; a latency without a percentile can conceal slow users; a similarity score without a labeled task can conceal irrelevant neighbors. Compare the observed value with a threshold chosen before seeing the final test result.

Now test the tempting shortcut. Suppose the team optimizes only the most visible metric. The result may look better while the system becomes less trustworthy. The reason is concrete: Training is expensive but amortized across many uses and can change persistent behavior. Inference is repeated per request and dominates operating cost at scale. Fine-tuning can improve stable task behavior but is slower to update than retrieval; retrieval gives fresh facts without changing weights. This is why the decision record must include both the intended gain and the tolerated regression. If the tolerated regression is unknown, the change is not ready for a consequential workflow.

• Prefer a measured baseline over a persuasive demo.
• Keep versions, inputs, and thresholds reproducible.
• Separate syntactic success from semantic correctness and authorization.
• Escalate or abstain when evidence falls outside the contract.
• Re-evaluate when data, traffic, models, providers, or user goals change.

These rules turn the topic into an engineering decision rather than a slogan. They also make disagreement productive: another person can challenge the assumptions, rerun the evaluation, and reach a documented conclusion.

At each mechanism step, annotate three things: the shape or type of data entering, the state read or written, and the possible error returned. Then ask whether rerunning that step is deterministic, probabilistic, or dependent on external state. This exposes bugs hidden by a successful final response. A useful trace includes versions and units—for example, tokens rather than characters, milliseconds at a named percentile, or vectors produced by a named embedding version. When an intermediate value cannot be observed directly, record the proxy and explain why it is informative.