C4: Container, Code, Cloud & Context
Designing intelligent systems, one layer at a time. ~ Nithin Mohan T K
Throughout my two decades in machine learning and AI systems, few developments have captured my imagination quite like the convergence of meta-learning with generative models. The ability to teach machines not just to learn, but to learn how to learn efficiently from minimal examples, represents a fundamental shift in how we approach AI system design. […]
Read more →Introduction: LLMs have no inherent memory—each request starts fresh. Building effective memory systems enables conversations that span sessions, personalization based on user history, and agents that learn from past interactions. Memory architectures range from simple conversation buffers to sophisticated vector-based long-term storage with semantic retrieval. This guide covers practical memory patterns: conversation buffers, sliding windows, […]
Read more →Introduction: How do you know if your LLM application is actually working? Evaluation is one of the most challenging aspects of building AI systems—unlike traditional software where tests pass or fail, LLM outputs exist on a spectrum of quality. This guide covers the essential metrics, benchmarks, and tools for evaluating LLMs, from automated metrics like […]
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The Weight of Responsibility After two decades of building enterprise systems, I have witnessed technology transform industries in ways that seemed impossible when I started my career. But nothing has challenged my understanding of responsible engineering quite like the emergence of generative AI. The systems we build today can create content indistinguishable from human work, […]
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The Reality Check We All Need The first time I encountered a hallucination in a production AI system, it cost my client three days of debugging and a significant amount of trust. A customer-facing chatbot had confidently provided detailed instructions for a product feature that simply did not exist. The response was articulate, well-structured, and […]
Read more →Introduction: Hardcoded prompts are a maintenance nightmare. When prompts are scattered across your codebase as string literals, updating them requires code changes, testing, and deployment. Prompt templates solve this by separating prompt logic from application code. This guide covers building a robust prompt template system: variable substitution, conditional sections, template inheritance, version control, and A/B […]
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