Introduction: Memory is what transforms a stateless LLM into a contextually aware assistant. Without memory, every interaction starts from scratch—the model has no knowledge of previous conversations, user preferences, or accumulated context. This guide covers the memory architectures that enable persistent, intelligent AI systems: conversation buffers for recent context, summary memory for long conversations, vector-based […]
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Tool Use and Function Calling: Extending LLM Capabilities with External Actions
Introduction: Function calling transforms LLMs from text generators into action-taking agents. Instead of just producing text responses, models can now decide when to call external functions, APIs, or tools to accomplish tasks. This capability enables building assistants that can search the web, query databases, send emails, execute code, and interact with any system that exposes […]
Read more →Structured Output from LLMs: Instructor Library and Production Patterns (Part 2 of 2)
Introduction: Getting LLMs to return structured data instead of free-form text is essential for building reliable applications. Whether you need JSON for API responses, typed objects for downstream processing, or specific formats for data extraction, structured output techniques ensure consistency and parseability. This guide covers the major approaches: JSON mode, function calling, the Instructor library, […]
Read more →LLM Deployment Strategies: From Model Optimization to Production Scaling
Introduction: Deploying LLMs to production is fundamentally different from deploying traditional ML models. The models are massive, inference is computationally expensive, and latency requirements are stringent. This guide covers the strategies that make LLM deployment practical: model optimization techniques like quantization and pruning, inference serving with batching and caching, containerization with GPU support, auto-scaling based […]
Read more →LLM Fine-Tuning Techniques: From LoRA to Full Parameter Training
Introduction: Fine-tuning transforms general-purpose LLMs into specialized models that excel at your specific tasks. While prompting can get you far, fine-tuning unlocks capabilities that prompting alone cannot achieve: consistent output formats, domain-specific knowledge, reduced latency from shorter prompts, and behavior that would require extensive few-shot examples. This guide covers the practical aspects of LLM fine-tuning: […]
Read more →LLM Testing and Evaluation: Building Confidence in AI Applications
Introduction: LLM applications are notoriously hard to test. Outputs are non-deterministic, “correct” is often subjective, and traditional unit tests don’t apply. Yet shipping untested LLM features is risky—prompt changes can break functionality, model updates can degrade quality, and edge cases can embarrass your product. This guide covers practical testing strategies: building evaluation datasets, implementing automated […]
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