AI Tools for Building and Deploying AI Features
Building AI into a product — not just using AI tools — requires a different set of decisions: which model to use, how to manage latency and cost, how to handle failures, and how to evaluate output quality at scale. The tools in this space are maturing fast.
How teams typically do this
Best AI tools to build ai-powered products
The leading API for building AI applications that require strong reasoning, careful instruction following, and safe outputs. Best in class for tasks that need nuance, long context, and reliability.
The most widely integrated AI API with the largest ecosystem of tools, libraries, and documentation. GPT-4o's speed and multimodal capabilities make it the default choice for most product teams.
The most popular framework for building LLM-powered applications. Handles retrieval-augmented generation, chaining, agents, and memory so you don't have to build everything from scratch.
Prompts to get started
Think through an AI feature properly before building it — catches the hard decisions early.
Help me design an AI feature for my product. Product: [describe what your product does] Feature idea: [describe what you want the AI to do] User: [who will interact with this feature and when?] Input: [what data or text will the AI receive?] Desired output: [what should it produce?] Constraints: [latency requirements, cost per call budget, privacy concerns] Please help me think through: 1. Whether this feature makes sense to build with AI (vs rule-based logic) 2. Which AI approach is most appropriate (LLM, classification, extraction, generation) 3. What the core prompt or system prompt should contain 4. Key failure modes and how to handle them gracefully 5. How to evaluate whether it's working (what does 'good' look like?) 6. The simplest version to build first
The system prompt determines how your AI feature behaves.
Write a system prompt for an AI feature. What this AI does: [describe — e.g. writes email replies for support team] User: [end user / internal employee / automated pipeline] Input: [what it receives] Expected output: [what it should produce] Tone and style: [how should it write?] Must always do: [requirements] Must never do: [constraints] Edge cases: [tricky situations] Please write: 1. A complete system prompt ready to use 2. Explanation of each section and reasoning 3. 3 test prompts to evaluate if it's working 4. Things to watch for that indicate it needs tuning
Decide how to measure whether your AI feature is working before shipping.
Design an evaluation framework for an AI feature. Feature: [describe] Inputs and outputs: [describe] What 'good' looks like: [how would an expert judge a great output?] What 'bad' looks like: [examples of failures] Scale: [outputs per day] Please design an eval framework with: 1. Automated metrics (no human review needed) 2. Human eval criteria: rubric with scoring dimensions 3. Golden set: how to build reference input/output pairs 4. Sample size per evaluation cycle 5. Decision rule: at what quality threshold do you ship, watch, or roll back?
Model your feature's costs before they surprise you in production.
Estimate and optimise API costs for an AI feature. Model: [Claude Sonnet / GPT-4o / Gemini] Pricing: [$/million input tokens · $/million output tokens] Typical prompt: [describe or paste example] Typical output: [length and type] Expected usage: - Requests per day: [number] - Peak usage: [describe spikes] Please: 1. Estimate monthly token usage and cost 2. Cost at 10x and 100x scale 3. Biggest cost driver (input / output / volume) 4. 3 ways to reduce cost without degrading quality 5. Caching strategy if applicable
Think through an AI feature properly before building it — catches the hard decisions early.
Help me design an AI feature for my product. Product: [describe what your product does] Feature idea: [describe what you want the AI to do] User: [who will interact with this feature and when?] Input: [what data or text will the AI receive?] Desired output: [what should it produce?] Constraints: [latency requirements, cost per call budget, privacy concerns] Please help me think through: 1. Whether this feature makes sense to build with AI (vs rule-based logic) 2. Which AI approach is most appropriate (LLM, classification, extraction, generation) 3. What the core prompt or system prompt should contain 4. Key failure modes and how to handle them gracefully 5. How to evaluate whether it's working (what does 'good' look like?) 6. The simplest version to build first
The system prompt determines how your AI feature behaves.
Write a system prompt for an AI feature. What this AI does: [describe — e.g. writes email replies for support team] User: [end user / internal employee / automated pipeline] Input: [what it receives] Expected output: [what it should produce] Tone and style: [how should it write?] Must always do: [requirements] Must never do: [constraints] Edge cases: [tricky situations] Please write: 1. A complete system prompt ready to use 2. Explanation of each section and reasoning 3. 3 test prompts to evaluate if it's working 4. Things to watch for that indicate it needs tuning
Decide how to measure whether your AI feature is working before shipping.
Design an evaluation framework for an AI feature. Feature: [describe] Inputs and outputs: [describe] What 'good' looks like: [how would an expert judge a great output?] What 'bad' looks like: [examples of failures] Scale: [outputs per day] Please design an eval framework with: 1. Automated metrics (no human review needed) 2. Human eval criteria: rubric with scoring dimensions 3. Golden set: how to build reference input/output pairs 4. Sample size per evaluation cycle 5. Decision rule: at what quality threshold do you ship, watch, or roll back?
Model your feature's costs before they surprise you in production.
Estimate and optimise API costs for an AI feature. Model: [Claude Sonnet / GPT-4o / Gemini] Pricing: [$/million input tokens · $/million output tokens] Typical prompt: [describe or paste example] Typical output: [length and type] Expected usage: - Requests per day: [number] - Peak usage: [describe spikes] Please: 1. Estimate monthly token usage and cost 2. Cost at 10x and 100x scale 3. Biggest cost driver (input / output / volume) 4. 3 ways to reduce cost without degrading quality 5. Caching strategy if applicable