Topic
llm
54 articles across 11 sub-topics
Sub-topic
29 articles
Chain of Thought Prompting: Teaching LLMs to Think Step by Step
TLDR: Chain of Thought (CoT) prompting tells a language model to reason out loud before answering. By generating intermediate steps, the model steers itself toward correct conclusions — turning guesswork into structured reasoning. It's the difference...
LLM Hallucinations: Causes, Detection, and Mitigation Strategies
TLDR: LLMs hallucinate because they are trained to predict the next plausible token — not the next true token. Understanding the three hallucination types (factual, faithfulness, open-domain) plus the five root causes lets you choose the right mitiga...
How AI Coding Agents Work: Models, Context, Sessions, and Memory
TLDR: An AI coding agent is an LLM stapled to a tool registry, wrapped in an orchestration loop that painstakingly rebuilds state on every single API call — because the model itself is completely stateless. Understanding the context window, the ReAct...
Types of LLM Quantization: By Timing, Scope, and Mapping
TLDR: There is no single "best" LLM quantization. You classify and choose quantization along three axes: when you quantize (timing), what you quantize (scope), and how values are encoded (mapping). In practice, most teams start with weight quantizati...
Practical LLM Quantization in Colab: A Hugging Face Walkthrough
TLDR: This is a practical, notebook-style quantization guide for Google Colab and Hugging Face. You will quantize real models, run inference, compare memory/latency, and learn when to use 4-bit NF4 vs safer INT8 paths. 📖 What You Will Build in Thi...
GPTQ vs AWQ vs NF4: Choosing the Right LLM Quantization Pipeline
TLDR: GPTQ, AWQ, and NF4 all shrink LLMs, but they optimize different constraints. GPTQ focuses on post-training reconstruction error, AWQ protects salient weights for better quality at low bits, and NF4 offers practical 4-bit compression through bit...
Sub-topic
13 articles
RAG vs Fine-Tuning: When to Use Each (and When to Combine Them)
TLDR: RAG gives LLMs access to current knowledge at inference time; fine-tuning changes how they reason and write. Use RAG when your data changes. Use fine-tuning when you need consistent style, tone, or domain reasoning. Use both for production assi...
Build vs Buy: Deploying Your Own LLM vs Using ChatGPT, Gemini, and Claude APIs
TLDR: Use the API until you hit $10K/month or a hard data privacy requirement. Then add a semantic cache. Then evaluate hybrid routing. Self-hosting full model serving is only cost-effective at > 50M tokens/day with a dedicated MLOps team. The build ...
LangChain Tools and Agents: The Classic Agent Loop
🎯 Quick TLDR: The Classic Agent Loop TLDR: LangChain's @tool decorator plus AgentExecutor give you a working tool-calling agent in about 30 lines of Python. The ReAct loop — Thought → Action → Observation — drives every reasoning step. For simple l...
LangChain 101: Chains, Prompts, and LLM Integration
TLDR: LangChain's LCEL pipe operator (|) wires prompts, models, and output parsers into composable chains — swap OpenAI for Anthropic or Ollama by changing one line without touching the rest of your code. 📖 One LLM API Today, Rewrite Tomorrow: The...
LangGraph Tool Calling: ToolNode, Parallel Tools, and Custom Tools
TLDR: Wire @tool, ToolNode, and bind_tools into LangGraph for agents that call APIs at runtime. 📖 The Stale Knowledge Problem: Why LLMs Need Runtime Tools Your agent confidently tells you the current stock price of NVIDIA. It's from its training d...
Streaming Agent Responses in LangGraph: Tokens, Events, and Real-Time UI Integration
TLDR: Stream agents token by token with astream_events; wire to FastAPI SSE for zero-spinner UX. 📖 The 25-Second Spinner: Why Streaming Is a UX Requirement, Not a Nice-to-Have Your agent takes 25 seconds to respond. Users abandon after 8 seconds....
Sub-topic
2 articles
Fine-Tuning LLMs with LoRA and QLoRA: A Practical Deep-Dive
TLDR: LoRA freezes the base model and trains two tiny matrices per layer — 0.1 % of parameters, 70 % less GPU memory, near-identical quality. QLoRA adds 4-bit NF4 quantization of the frozen base, enabling 70B fine-tuning on 2× A100 80 GB instead of 8...
How GPT (LLM) Works: The Next Word Predictor
TLDR: At its core, GPT asks one question, repeated: "Given everything so far, what is the most likely next token?" Tokens are not words — they're subword units. The Transformer architecture uses self-attention to weigh how much each token should infl...
Sub-topic
2 articles
Sparse Mixture of Experts: How MoE LLMs Do More With Less Compute
TLDR: Mixture of Experts (MoE) replaces the single dense Feed-Forward Network (FFN) layer in each Transformer block with N independent expert FFNs plus a learned router. Only the top-K experts activate per token — so total parameters far exceed activ...
Dense LLM Architecture: How Every Parameter Works on Every Token
TLDR: In a dense LLM every single parameter is active for every token in every forward pass — no routing, no selection. A transformer block runs multi-head self-attention (Q, K, V) followed by a feed-forward network (FFN) with roughly 4× the hidden d...
Sub-topic
2 articles
LLM Skills vs Tools: The Missing Layer in Agent Design
TLDR: A tool is a single callable capability (search, SQL, calculator). A skill is a reusable mini-workflow that coordinates multiple tool calls with policy, guardrails, retries, and output structure. If you model everything as "just tools," your age...
LLM Skill Registries, Routing Policies, and Evaluation for Production Agents
TLDR: If tools are primitives and skills are reusable routines, then the skill registry + router + evaluator is your production control plane. This layer decides which skill runs, under what constraints, and how you detect regressions before users do...
Sub-topic
1 article
Fine-Tuning LLMs: The Complete Engineer's Guide to SFT, LoRA, and RLHF
TLDR: A pretrained LLM is a generalist. Fine-tuning makes it a specialist. Supervised Fine-Tuning (SFT) teaches it your domain's language through labeled examples. LoRA does the same with 99% fewer trainable parameters. RLHF shapes its behavior using...
Sub-topic
1 article
LLM Software Development Pitfalls: What to Avoid and When to Simplify
TLDR: Most bad LLM products do not fail because the model is weak. They fail because teams wrap a maybe-useful model in too much architecture: prompt spaghetti, no eval harness, weak tool schemas, huge context windows, agent chains nobody can explain...
Sub-topic
1 article
LLM Observability: Tracing, Logging, and Debugging Production AI Systems
TLDR: 🔍 LLM observability is radically different from traditional APM—non-deterministic outputs, variable token costs, and multi-step reasoning chains require specialized tracing. LangSmith provides native LangChain integration, OpenTelemetry offers...
Sub-topic
1 article
LLM Evaluation Frameworks: How to Measure Model Quality (RAGAS, DeepEval, TruLens)
TLDR: 📏 Traditional ML metrics (accuracy, F1) fail for LLMs because there's no single "correct" answer. RAGAS measures RAG pipeline quality with faithfulness, answer relevance, and context precision. DeepEval provides unit-test-style LLM evaluation....
Sub-topic
1 article
LangChain RAG: Retrieval-Augmented Generation in Practice
⚡ TLDR: RAG in 30 Seconds TLDR: RAG (Retrieval-Augmented Generation) fixes the LLM knowledge-cutoff problem by fetching relevant documents at query time and injecting them as context. With LangChain you build the full pipeline — load → split → embed...
Sub-topic
1 article
LangChain Memory: Conversation History and Summarization
TLDR: LLMs are stateless — every API call starts fresh. LangChain memory classes (Buffer, Window, Summary, SummaryBuffer) explicitly inject history into each call, and RunnableWithMessageHistory is the modern LCEL replacement for the legacy Conversat...