Navaneethan L·4d agoIntegrating AI into the SDLC: Engineering Practices for Teams That Ship AI FeaturesThe unit tests pass. CI is green. You deploy.
Navaneethan L·Nov 7The AI Development Lifecycle (ADLC): From Problem to ProductionThe software development lifecycle is built on one assumption: predictability.
Navaneethan L·Nov 6AI Technical Debt: Managing What AI Systems AccumulateTraditional technical debt announces itself.
Navaneethan L·Nov 6Context Engineering at ScaleThe context window is RAM. Nobody wrote a memory manager.
Navaneethan L·Nov 5Retrieval System Design: Beyond the BasicsThe “chunk the PDF, embed it, stuff the top 3 chunks into the prompt” pipeline has a well-defined ceiling.
Navaneethan L·Nov 5AI Security: Prompt Injection, Adversarial Inputs, and DefenseThe most dangerous attack on your enterprise AI stack will not target the model. It will target the descriptions of the tools your model…A response icon2A response icon2
Navaneethan L·Nov 5Prompt Engineering at Production ScalePrompt engineering split into two disciplines somewhere between 2024 and 2026, and most practitioners are only working one of them.
Navaneethan L·Nov 4Evaluation Engineering: Building Evals That Actually WorkMost AI teams have evals. Most AI teams’ evals do not work.A response icon1A response icon1
Navaneethan L·Nov 4Agent Design Patterns: A Production TaxonomyReAct is not an agent design pattern. It is a reasoning loop.