Introduction: Tool use transforms LLMs from text generators into capable agents that can search the web, query databases, execute code, and interact with APIs. But implementing tool use well is tricky—models hallucinate tool calls, pass invalid arguments, and struggle with multi-step tool chains. The difference between a demo and production system lies in robust tool […]
Read more →Category: Artificial Intelligence(AI)
Retrieval Augmented Generation Patterns: Building RAG Systems That Actually Work
Introduction: Retrieval Augmented Generation (RAG) grounds LLM responses in your actual data, reducing hallucinations and enabling knowledge that wasn’t in the training set. But naive RAG—embed documents, retrieve top-k, stuff into prompt—often disappoints. Retrieval misses relevant documents, context windows overflow, and the model ignores important information buried in long contexts. This guide covers advanced RAG […]
Read more →LLM Output Parsing: Extracting Structured Data from Free-Form Text
Introduction: LLMs generate text, but applications need structured data—JSON objects, lists, specific formats. The gap between free-form text and usable data structures is where output parsing comes in. Naive approaches using regex or string splitting break constantly as models vary their output format. Robust parsing requires multiple strategies: format instructions that guide the model, extraction […]
Read more →Prompt Compression: Fitting More Context into Your Token Budget
Introduction: Context windows are precious real estate. Every token you spend on context is a token you can’t use for output or additional information. Long prompts hit token limits, increase latency, and cost more money. Prompt compression techniques help you fit more information into less space without losing the signal that matters. This guide covers […]
Read more →Multi-Modal LLM Integration: Building Applications with Vision Capabilities
Introduction: Modern LLMs understand more than text. GPT-4V, Claude 3, and Gemini can process images alongside text, enabling applications that reason across modalities. Building multi-modal applications requires handling image encoding, managing mixed-content prompts, and designing interactions that leverage visual understanding. This guide covers practical patterns for integrating vision capabilities: encoding images for API calls, building […]
Read more →LLM Evaluation Metrics: Measuring Quality Beyond Human Intuition
Introduction: How do you know if your LLM application is working well? Subjective assessment doesn’t scale, and traditional NLP metrics often miss what matters for generative AI. Effective evaluation requires multiple approaches: reference-based metrics that compare against gold standards, semantic similarity that measures meaning preservation, and LLM-as-judge techniques that leverage AI to assess AI. This […]
Read more →