Introduction: Command-line tools are the developer’s natural habitat. Adding LLM capabilities to CLI tools creates powerful utilities for code generation, documentation, data transformation, and automation. Unlike web apps, CLI tools are fast to build, easy to integrate into existing workflows, and perfect for power users who live in the terminal. This guide covers building production-quality […]
Read more →Author: Nithin Mohan TK
Hello Namaste! 👋🏻
I'm an accomplished Enterprise Solution Architect & Engineering Leader with 20+ years of progressive experience architecting, building, and scaling transformative enterprise solutions.
⚜️ Solution Architect / AI & Cloud 📌 Galway, Ireland
Multi-Modal AI: Building Applications with Vision, Audio, and Text
Introduction: Multi-modal AI combines text, images, audio, and video understanding in a single model. GPT-4V, Claude 3, and Gemini can analyze images, extract text from screenshots, understand charts, and reason about visual content. This guide covers building multi-modal applications: image analysis and description, document understanding with vision, combining OCR with LLM reasoning, audio transcription and […]
Read more →Context Window Management: Token Budgets, Prioritization, and Compression
Introduction: Context windows define how much information an LLM can process at once—from 4K tokens in older models to 128K+ in modern ones. Effective context management means fitting the most relevant information within these limits while leaving room for generation. This guide covers practical context window strategies: token counting and budget allocation, content prioritization, compression […]
Read more →Meta-Learning for Few-Shot Image Generation using GPT-3 | Generative-AI
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 →Memory Systems for LLMs: Buffers, Summaries, and Vector Storage
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 →LLM Evaluation: Metrics, Benchmarks, and Testing Strategies That Actually Work
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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