Designing an AI Feature End to End

Building an AI feature is not the same as calling an API endpoint and displaying the result. It involves a chain of decisions that affect quality, cost, latency, and maintainability: what task does the model perform, how is context assembled, how do outputs get validated, how does the system degrade gracefully under failure? Working through these questions systematically before writing code prevents expensive rearchitecting later.

Step 1: define the task precisely

The most common design mistake is starting with a vague task description. 'Summarize the article' leaves critical questions unanswered: how long should the summary be, what audience is it for, should it be in bullet points or prose, what should happen if the article is in a different language? Write a task specification that includes the desired output format, length constraints, tone, what to do in edge cases, and at least three concrete examples of good outputs. This becomes the foundation of your system prompt.

Step 2: map the data and context flow

Draw the data flow for a single request: where does the user's input come from, what context gets assembled and inserted into the prompt, how does the model's output get post-processed before reaching the user, and what gets logged for evaluation and debugging? Identifying every transformation in this chain reveals hidden dependencies and integration points that are easy to miss in code.

• User input: raw text, structured form data, file uploads, or audio transcription
• Context assembly: retrieved documents, user history, system state, tool outputs
• Prompt construction: template rendering, token budget enforcement, format instructions
• Model call: provider selection, parameter settings, retry logic
• Output processing: parsing, validation, formatting, fallback handling
• Logging: request/response pairs, latency, token counts, user feedback signals

Step 3: set quality and latency targets

Without measurable targets, you have no way to declare the feature ready to ship. Define an acceptable accuracy or quality score on your eval set, a p95 response latency budget, a cost-per-request ceiling, and an availability target. These targets drive every subsequent design decision: which model to use, whether to cache aggressively, how complex the context assembly can be.

# Example feature requirements captured as constants
MIN_QUALITY_SCORE = 0.80  # fraction of eval cases passing
P95_LATENCY_MS = 2000     # 95th percentile response time
MAX_COST_PER_REQUEST = 0.005  # USD
CACHE_TTL_SECONDS = 3600  # cache identical queries for 1 hour