A concise guide to AI engineering for web teams—prompting, tool use, safety, and delivery habits that keep AI features maintainable.
Scope the assistant
Define what the model is allowed to do, what tools it can call, and what must stay server-side. Narrow scopes reduce risk and make testing tractable for any AI engineer.
Ground responses
Prefer retrieval, structured context, or verified facts over pure generation when accuracy matters. This pattern shows up often in freelance AI developer engagements where the client cannot afford confident mistakes.
Version prompts like code
Track prompt changes, expected behavior, and regression tests. Small diffs are easier to review than monolithic prompt blobs.
Performance and cost
Cache stable completions where policy allows, batch when possible, and measure tokens per user journey. Full stack developer habits—profiling, tracing, and budgets—apply directly to LLM workloads.
Learn more
For more writing from Gokulakrishnan, see the other posts on this blog or browse the open-source and product sections on the homepage.