--- title: How to Build an AI Feature With Gemini: A Practical... description: A deep-dive into the real work of shipping an AI feature with Gemini, going far beyond basic API calls. Covers when not to use AI (regex vs. model call... canonical: https://daily.dev/posts/how-to-build-an-ai-feature-with-gemini-a-practical-guide-to-prompt-engineering-for-developers-je89vebi7 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: How to Build an AI Feature With Gemini: A Practical Guide to Prompt Engineering for Developers | daily.dev og:description: A deep-dive into the real work of shipping an AI feature with Gemini, going far beyond basic API calls. Covers when not to use AI (regex vs. model call... og:url: https://daily.dev/posts/how-to-build-an-ai-feature-with-gemini-a-practical-guide-to-prompt-engineering-for-developers-je89vebi7 og:image: https://api.daily.dev/og/posts/jE89VebI7.png og:image:alt: How to Build an AI Feature With Gemini: A Practical Guide to Prompt Engineering for Developers og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # How to Build an AI Feature With Gemini: A Practical Guide to Prompt Engineering for Developers **[freeCodeCamp](https://daily.dev/sources/freecodecamp)** · 32 min read · 1 upvotes · 0 comments ## Summary A deep-dive into the real work of shipping an AI feature with Gemini, going far beyond basic API calls. Covers when not to use AI (regex vs. model call tradeoffs), model selection and config management, iterative prompt engineering with negative examples and counterexamples, structured JSON output with defensive parsing, manual QA checklists with expected ranges instead of exact outputs, retry logic with model fallback, and graceful error handling. Key lessons include: banning specific failure phrases rather than describing desired behavior, separating prompt concerns to avoid constraint collisions, using both rules and few-shot examples together, and placing deterministic checks in front of the model for safety-critical paths. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### When should I use an LLM instead of writing regular code for a feature like tagging or classification? Use a model only when the input is unbounded and the output requires judgment; if either condition is missing, write the code instead. A topic-tagging feature with a small, stable vocabulary was implemented as a regex instead of a Gemini call, since latency (~0ms vs 300-800ms), cost, and predictability of failures all favored the deterministic approach for a feature where a wrong tag is nearly harmless. _Developers weighing AI versus deterministic code for a feature can compare real tradeoffs like this on daily.dev._ ### Why does my LLM chatbot keep inventing details or assumptions the user never stated? A generic, invented response usually means the model answered the statistical average of the input rather than the actual input, not a style failure. For example, a message about thinking of an old job was answered as if it implied regret and second-guessing, though the user stated neither. Fixing tone alone leaves confident-sounding fabrication; the fix is checking for and banning invented assumptions directly. _Anyone debugging LLM prompt drift can track fixes like projection-checking on daily.dev._ ### How do I safely parse JSON responses from Gemini or another LLM API that sometimes wraps output in markdown or adds extra text? Extract the JSON before parsing using a greedy regex like /\{[\s\S]*\}/ to grab from the first { to the last }, discarding markdown fences or commentary, then wrap JSON.parse in a try/catch and degrade to returning the raw text under a fallback key instead of failing outright, as long as a human will read the result rather than downstream code trusting it as valid data. _Developers hardening AI features against malformed output can compare parsing strategies on daily.dev._ ## Similar posts on daily.dev - [Google Prompt Engineering for App Development: Best Practices](https://daily.dev/posts/google-prompt-engineering-for-app-development-best-practices-hw06uccfm) · PromptLayer Blog · 1 upvotes · 0 comments - [Build intelligent Android apps: On-device inference](https://daily.dev/posts/build-intelligent-android-apps-on-device-inference-kynhfi0rx) · Android Developers Blog · 0 upvotes · 0 comments - [How to Build Production-Ready AI Features with Flutter \[Full Handbook for Devs\]](https://daily.dev/posts/how-to-build-production-ready-ai-features-with-flutter-full-handbook-for-devs--dvqqtnhpi) · freeCodeCamp · 3 upvotes · 0 comments - [Gemini API in Ruby: Building AI Agents Without an SDK](https://daily.dev/posts/gemini-api-in-ruby-building-ai-agents-without-an-sdk-7psccge0x) · Ruby Flow · 1 upvotes · 0 comments --- Tags: [#javascript](https://daily.dev/tags/javascript), [#llm](https://daily.dev/tags/llm), [#prompt-engineering](https://daily.dev/tags/prompt-engineering), [#google-gemini](https://daily.dev/tags/google-gemini) [View this post on daily.dev](https://daily.dev/posts/how-to-build-an-ai-feature-with-gemini-a-practical-guide-to-prompt-engineering-for-developers-je89vebi7) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"How to Build an AI Feature With Gemini: A Practical Guide to Prompt Engineering for Developers","url":"https://daily.dev/posts/how-to-build-an-ai-feature-with-gemini-a-practical-guide-to-prompt-engineering-for-developers-je89vebi7","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/how-to-build-an-ai-feature-with-gemini-a-practical-guide-to-prompt-engineering-for-developers-je89vebi7"},"datePublished":"2026-07-22T21:31:21.671Z","dateModified":"2026-09-13T20:30:31.706Z","description":"A deep-dive into the real work of shipping an AI feature with Gemini, going far beyond basic API calls. 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