How do I add AI to my existing app?

Adding AI to an existing product is typically faster and less disruptive than founders expect. The key is starting with a single, well-defined use case rather than a general "AI layer."

Step 1 -- Define the AI Use Case

Pick one specific user problem to solve with AI. Good first AI features share common characteristics: the user currently does something manually that could be automated, the input data is already in your system, and success is easy to measure (time saved, accuracy improved, user retention improved). Avoid AI features defined as "like ChatGPT but for [your product]" -- these are scope traps.

Step 2 -- Choose the Integration Pattern

• LLM API call from backend -- your server calls an LLM API (Claude, OpenAI), passes context from your DB, gets a structured response. Best for document analysis, content generation, classification, Q&A over your data.
• RAG (Retrieval-Augmented Generation) -- embed your documents/records in a vector database, retrieve relevant context at query time, pass to the LLM. Best for large knowledge bases the LLM cannot hold in its context window.
• Fine-tuning -- train the model on your domain-specific data. Best when base models do not understand your specialized vocabulary or format. Significantly more expensive and slower than RAG.
• Streaming responses -- stream LLM output token-by-token to the frontend for a real-time feel. Requires server-sent events or WebSocket, but dramatically improves perceived performance.

Step 3 -- Instrument and Iterate

Log every LLM call with the prompt, context, output, and user feedback signal (thumbs up/down, did they edit the output?). This data is critical for improving prompt quality and identifying failure modes. Build the logging before you launch -- retrofitting it is painful.

Code and Trust integrates LLMs into existing products across all major stacks. See our AI implementation services or contact us to scope your AI feature.