How to Engineer AI Features

How to Engineer AI Features

Engineering AI features means turning an LLM idea into application logic that your team can test, ship, monitor, and improve. A working demo is useful, but production users will send messy inputs, trigger edge cases, wait on slow tools, and expose unclear product requirements.

This tutorial gives you a repeatable workflow for building prompts, agents, and LLM-powered workflows. It assumes you already know basic APIs, JSON, prompts, and software engineering practices.

• Inventing details that are not in the ticket
• Dropping important account or billing context
• Misclassifying urgency
• Returning too much text for the UI
• Failing on long threads
• Exposing internal notes that should stay hidden
• At least 95% of summaries include the customer’s core issue.
• At least 98% of outputs fit within 5 bullets.
• Less than 1% of outputs include unsupported claims.
• Median latency stays under 2 seconds.
• JSON parsing succeeds in at least 99.5% of requests if the feature uses structured output.
• Single prompt: Best for classification, rewriting, extraction, summarization, routing, and simple transformation.
• Structured output: Best when your application needs JSON, labels, scores, or typed fields.
• Retrieval-augmented generation: Best when the model needs private docs, product data, policies, or recent information.
• Prompt chain: Best when the task has clear stages, such as extract, validate, then write.
• Tool-calling workflow: Best when the model must query APIs, calculate values, search records, or update systems.
• Agent: Best when the model must choose between tools, inspect results, and continue until a goal is reached.
• Required fields
• Optional fields
• Allowed values
• Maximum lengths
• Fallback values
• What to do when confidence is low
• Role and task: What the model is doing
• Inputs: What data it will receive
• Rules: Hard constraints and safety requirements
• Output format: Exact schema or format
• Examples: Representative cases, including edge cases
• Common happy-path examples
• Long inputs
• Short ambiguous inputs
• Malformed or incomplete data
• Policy edge cases
• Adversarial or prompt-injection attempts when relevant
• Real production examples after proper redaction
• Factual accuracy from 1 to 5
• Completeness from 1 to 5
• Format compliance as pass or fail
• Unsupported claims as pass or fail
• Tone as pass or fail
• Request validation before the model call
• Prompt version selection
• Model and parameter configuration
• Timeouts
• Retries for transient failures
• Structured output validation
• Fallback behavior
• Logging and tracing
• Cost and latency tracking
• Schema validation: Reject outputs that do not match the expected structure.
• Enum validation: Reject labels outside your allowed set.
• Confidence thresholds: Route low-confidence results to a safer path.
• Policy checks: Block outputs that violate product, legal, or safety rules.
• Tool permissions: Limit which tools an agent can call and what each tool can modify.
• Read-before-write flows: Require the model to inspect current state before taking action.
• Approval gates: Require a person to approve high-impact actions, such as refunds, account closure, or outbound messages.
• Prompt version
• Model name
• Model parameters
• Input variables
• Retrieved documents or tool results
• Final model output
• Parsed output
• Validation errors
• Latency
• Token usage
• Cost estimate
• User or account identifier, when allowed by your privacy rules
• Create or edit a prompt version.
• Run the eval suite against the current production version and the candidate version.
• Compare pass rates, latency, token usage, and cost.
• Inspect regressions manually.
• Approve the new version if it meets your thresholds.
• Roll out gradually.
• JSON validity must be at least 99.5%.
• Classification accuracy must not drop by more than 1 percentage point.
• Unsupported claims must stay below 1% on the eval set.
• Median latency must stay under 2 seconds.
• Average cost per request must stay below $0.01.
• Internal testing with real workflows
• 1% of eligible traffic
• 10% after metrics look stable
• 50% after support and product teams review sampled outputs
• 100% after the feature meets quality, latency, and cost targets
• Task completion rate
• Validation failure rate
• Fallback rate
• User edits or corrections
• Thumbs up or thumbs down feedback
• Latency by step
• Cost by account, feature, or workflow
• Support tickets caused by the AI feature
• Review sampled traces weekly for new features.
• Group failures by cause, such as prompt issue, missing context, model limitation, bad tool result, or unclear product rule.
• Add representative failures to the eval dataset.
• Update prompts, retrieval, tools, or product rules based on the root cause.
• Compare the new version against the current version before release.

Use production failures to improve the system

Every production failure should become a useful artifact. Add failed or low-quality examples to your dataset after redaction. Label the expected behavior. Run them against future prompt and model changes.Create a regular review loop:This is where AI engineering becomes repeatable. Your team stops guessing whether a prompt is better and starts measuring behavior against real cases.

Ship with feature flags, sampling, and rollback

Do not send 100% of production traffic to a new AI behavior on day one. Use a staged rollout.A common rollout plan:Keep rollback simple. Your team should be able to switch back to a previous prompt version, model, or non-AI fallback quickly.Track production metrics such as:

Run evals in CI and before prompt releases

Prompts change application behavior. Treat prompt releases with the same care you apply to code releases.A practical release process looks like this:Set minimum thresholds before the release. For example:These thresholds will vary by use case. A marketing copy assistant can tolerate more variation than an AI workflow that updates billing records.

Trace every request end to end

When an AI feature fails, you need to know what happened. Store enough detail to debug without exposing sensitive data to people who should not see it.At minimum, capture:For a chain or agent, trace each step separately. If a customer email generator writes the wrong response, the root cause might be bad retrieval, a stale policy document, a weak prompt, or an incorrect tool result. A single final output log will not tell you enough.

Add guardrails for unsafe, low-confidence, and out-of-scope cases

Guardrails should protect the product experience and downstream systems. They do not need to be complicated to be useful.Common guardrails include:For agents, tool design is one of your strongest safety controls. Use narrow tools with typed arguments. A tool named update_customer_record is broad. A tool named add_internal_ticket_note with a 500-character limit is easier to reason about.

Implement the AI workflow like production code

Your AI feature should have the same engineering basics as the rest of your system.Build a service layer around model calls. Avoid scattering direct LLM calls across controllers, jobs, and frontend code.A reliable implementation usually includes:For example, if a ticket classifier returns invalid JSON, your code might retry once with a repair prompt. If it still fails, route the ticket to the default queue and record the failure for later analysis.Be careful with retries. Retrying a slow model call three times can turn a 4-second feature into a 12-second feature. Set a clear latency budget before launch.

Create an eval dataset before you tune the prompt

Do not rely on five hand-picked examples in a chat window. Build a small eval dataset early. Start with 30 to 100 cases. Add more as the feature gets closer to production.Your eval set should include:For classification and extraction, use expected outputs. For summarization or writing tasks, use rubrics. A rubric might score:When possible, combine automated checks with review by a person. For example, you can automatically check JSON validity and length, then have reviewers inspect factual accuracy on sampled outputs.

Write prompts as versioned application logic

A production prompt should be readable, testable, and versioned. Avoid one long blob that mixes task instructions, policies, examples, formatting rules, and runtime data.A useful prompt structure is:Example:

You classify customer support tickets for a SaaS billing team.

Return JSON only.

Allowed categories:
- billing_dispute
- cancellation_request
- account_access
- technical_bug
- feature_request
- other

Rules:
- Use only information from the ticket.
- If the ticket contains multiple issues, choose the category that requires the fastest response.
- Set urgency to "high" only when the customer reports loss of access, failed payment for an active account, legal threat, or repeated unresolved billing issue.
- If the ticket is unclear, use category "other" and confidence below 0.6.

Output schema:
{
  "category": string,
  "urgency": "low" | "medium" | "high",
  "confidence": number,
  "reason": string
}

Keep prompts close to the product behavior they control. If the refund policy changes, your team should know which prompt, eval set, and release path need updates.

Design the input and output contract

Treat the model as a service boundary. Define exactly what goes in and what must come out.For a ticket classification feature, your output contract might look like this:

{
  "category": "billing_dispute",
  "urgency": "high",
  "confidence": 0.86,
  "reason": "The customer says they were charged twice and asks for an immediate refund.",
  "needs_human_review": false
}

Use enums where possible. Open-ended strings are harder to test and easier to misuse downstream.Define:If your application depends on valid JSON, enforce it at the model API level when supported. Then validate it again in your code. The model output is still external input.

Choose the AI interaction pattern

Pick the simplest pattern that can complete the task reliably. Many AI features do not need an autonomous agent.Use this decision path:For example, a “refund eligibility assistant” probably needs retrieval and tool calls. It may need to fetch order data, check policy, calculate dates, and return a decision. A “rewrite this message in a professional tone” feature only needs a single prompt with clear constraints.Do not add agent behavior until you need it. Agents add state, tool safety concerns, retry logic, trace complexity, and harder evals.

Define the user task, failure modes, and success criteria

Start with the user task, not the model. Write down what the feature must do in plain language.Weak task definition:“Summarize support tickets.”Better task definition:“Given a support ticket thread, produce a 3 to 5 bullet summary for an agent who is about to reply. Include the customer’s issue, current plan, attempted fixes, urgency, and any unresolved questions.”Then define failure modes before you write the prompt. For a support ticket summary feature, common failures include:Now define measurable success criteria. Use numbers where you can:This gives your team something concrete to test against. It also prevents prompt changes from turning into subjective debates.

Example architecture for a reliable AI feature

Here is a practical architecture for a production LLM feature:

1. The application receives a user request.
2. Your service validates the request and loads the correct prompt version.
3. The service retrieves relevant context, if needed.
4. The model returns structured output.
5. Your code validates the schema and business rules.
6. The workflow calls tools or APIs only after validation.
7. The system records traces, inputs, outputs, latency, token usage, and errors.
8. Evals compare new versions against known test cases before release.
9. Production feedback creates new dataset examples.

This structure works for simple prompt features and more complex agent workflows. The main difference is the number of steps and tools involved.

Common mistakes when engineering AI features

• Starting with model choice instead of task design: A stronger model will not fix unclear requirements.
• Testing only with ideal examples: Real users send incomplete, long, emotional, and contradictory inputs.
• Skipping structured output: Free-form text is harder to validate and harder to connect to application logic.
• Letting agents use broad tools: Narrow tools reduce accidental damage and make traces easier to inspect.
• Changing prompts without evals: Small wording changes can create regressions.
• Ignoring cost until launch: A feature that works in testing can become too expensive at production volume.
• Logging too little: Without traces, your team cannot debug failures or compare versions well.

A practical checklist

Before you ship an AI feature, make sure your team can answer yes to these questions:

• Did we define the user task clearly?
• Did we list the main failure modes?
• Did we define measurable success criteria?
• Did we choose the simplest interaction pattern that works?
• Did we define a strict input and output contract?
• Did we version the prompt?
• Did we create an eval dataset with edge cases?
• Did we validate model outputs in code?
• Did we add fallback behavior?
• Did we trace each model call, retrieval step, and tool call?
• Did we compare the new version against the current version?
• Did we set up monitoring, sampling, and rollback?

If you can answer yes to most of these, you are much closer to shipping an AI feature your team can maintain.

Final thoughts

Reliable AI features come from clear task design, strict contracts, evals, tracing, and controlled releases. The model matters, but the workflow around the model usually decides whether the feature survives real users.

Start small. Build one well-scoped feature. Measure it. Add traces. Create evals. Then use the same process for the next prompt, chain, or agent.

PromptLayer helps AI teams manage prompts, run evaluations, trace LLM requests, compare versions, and improve production AI workflows. If you are building AI features and want a cleaner workflow for prompt management and observability, create a PromptLayer account.