Large Language Models
15 articles · written by Niraj Kumar
Most engineering writing about LLMs stops at the API call. These articles start after it — at the point where you have to choose between a hosted API and a local model, decide whether fine-tuning is worth it, keep token costs attributable per customer, and prove the model has not regressed since last week.
Topics here include fine-tuning with Unsloth and QLoRA, running local models with Ollama on ordinary developer hardware, evaluating and monitoring fine-tuned models in production, per-tenant token cost tracking with OpenTelemetry, privacy-first architectures for teams that cannot send data to third-party APIs, and lightweight on-device inference.
The recurring theme is that the interesting decisions are operational, not model-selection: what you measure, what you cache, what you keep on your own hardware, and what you are willing to pay per request.
- GraphRAGLangChainOllama
How to Build a Local GraphRAG Pipeline with PostgreSQL, LangChain, and Ollama for Multi-Hop Reasoning
Build a fully local GraphRAG system with LangChain, Ollama and PostgreSQL — extract entity triplets, store a knowledge graph, run multi-hop queries.
- RAGOllamaLangChain
How to Build a Local RAG Pipeline Using Ollama, LangChain, and PostgreSQL
Set up a private, offline RAG system by connecting locally hosted LLMs via Ollama to a PostgreSQL pgvector database using LangChain.
- Next.jsAI SecurityWeb Security
Securing Your AI-Powered Next.js Applications: Best Practices
Secure AI-powered Next.js apps — API key management, data privacy, input validation, and defence against prompt injection, SSRF and friends.
- LangGraphAI AgentsLangChain
Beyond Basic Chatbots: Developing Advanced AI Agents with LangGraph
Build stateful, multi-step AI agents with LangGraph — core concepts, real architectures, code examples, and the pitfalls beyond simple chatbots.
- RAGLangChainNext.js
Building a Production-Ready RAG System with LangChain and Next.js
Architect and build a production RAG system with LangChain for orchestration and Next.js for the UI — ingestion, retrieval, streaming, deployment.
- LLMPrivacyLocal Models
Privacy‑First LLM Apps: When to Use Local Models vs Cloud APIs
For B2B and regulated domains — sensitive data handling, compliance, latency trade-offs, and when a local model beats a cloud LLM API.
- AI AgentsLLMTool Calling
Designing AI Agents: Tools, Patterns, and Pitfalls
Designing AI agents that hold up — single-step vs multi-agent workflows, tool-calling patterns, memory architecture, and the reliability pitfalls.
- OllamaLLMLocal AI
Ollama in Practice: Running Local LLMs on Your Dev Machine
Install Ollama, pull and manage open-source LLMs, run terminal chats, and wire local models into Node.js and Python — no tokens leave your machine.
- LangChainOpenAIOllama
Building Your Own Chatbot with LangChain and OpenAI (or Ollama)
Build a production chatbot with LangChain and OpenAI or Ollama — document ingestion, vector stores, a RAG pipeline, and a deployable web interface.
- Graph-RAGKnowledge GraphsRAG
Graph‑RAG Explained: Building Smarter AI Agents with Knowledge Graphs
How Graph-RAG combines knowledge graphs with LLMs to improve multi-hop reasoning and accuracy — with code examples and enterprise use cases.
- LangChainLlamaIndexLLM
LangChain vs. LlamaIndex: Which One Should You Use in 2026?
LangChain vs LlamaIndex compared on architecture, use cases and developer experience — with code examples to help you pick an LLM framework.
- LLMFine-TuningHugging Face
Fine‑Tuning LLMs: Step‑by‑Step Guide for Developers
Fine-tune open-source LLMs like Llama and Mistral with PyTorch and Hugging Face — dataset prep, LoRA and QLoRA, training, evaluation, deployment.
- AILLMVS Code Extension
Build Your Own AI Coding Assistant: Integrating LLMs into VS Code or JetBrains
Build a real AI coding assistant for VS Code or JetBrains — LLM API integration, streaming completions, context-aware prompting, plugin architecture.
- RAGAILLM
Mastering RAG in Practice: Build a Production-Ready AI Search Feature for Your App
Build a production RAG system from scratch — architecture patterns, code examples, and what it takes to ship intelligent search inside your app.
- On-Device AIEdge AIMobile ML
On-Device AI in 2026: Running Lightweight Models in Mobile and Edge Apps
On-device AI inference for mobile and edge — model compression, runtime frameworks, deployment patterns, and the trade-offs before leaving the cloud.
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