What you will learn
- The model is one component of a larger system.
- Architecture should be driven by task risk and evidence needs.
- Evaluation and observability must be designed before scaling.
01
Begin with the task boundary
Define the input, user, decision, output, sources of truth, risk, and acceptable latency. This determines whether the product needs a simple prompt, retrieval, tool use, persistent state, or human approval.
Avoid adding architectural components because they are fashionable. Every component creates cost, latency, security surface, and maintenance work.
02
Separate the system layers
A maintainable system separates user experience, orchestration, model access, context preparation, retrieval, tools, policy enforcement, output validation, and telemetry. This makes failures diagnosable and components replaceable.
- Application and identity layer
- Task orchestration and business rules
- Model gateway and routing
- Context and retrieval layer
- Tool and data integrations
- Validation, policy, evaluation, and tracing
03
Control model variability
Use clear instructions, representative examples, structured output schemas, deterministic validators, and retry policies. Validate facts against authoritative sources when the task requires accuracy.
A lower temperature does not make a system truthful. Reliability comes from evidence, constraints, validation, and appropriate human review.
04
Design for model change
Place model-specific configuration behind a gateway. Keep evaluation sets and performance baselines so a model upgrade can be tested for quality, cost, latency, and safety before release.
Version the prompt, context strategy, model, and validators together. An unexplained model change can invalidate earlier test results.
05
Operate the system
Trace requests without exposing unnecessary sensitive data. Monitor latency, token use, tool errors, retrieval quality, validation failures, escalation, user corrections, and business outcomes.
Create an incident process and a kill switch for capabilities that can affect external systems. Feed verified production failures back into the evaluation set.
FAQ
Common questions
Do I need a vector database?
Only if semantic retrieval improves the defined task. Small, structured, or frequently changing datasets may be better served by direct database queries, search, or curated context.
Should I use one model for every task?
Not necessarily. Route tasks by quality, modality, latency, cost, context, and policy requirements, but avoid unnecessary routing complexity.
Turn the method into a reusable instruction.
Explore expert prompts