Category
llm
44 articles across 8 sub-topics
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. π 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...
SFT for LLMs: A Practical Guide to Supervised Fine-Tuning
TLDR: Supervised fine-tuning (SFT) is the stage where a pretrained model learns task-specific response behavior from curated input-output examples. It is usually the first alignment step after pretraining and often the foundation for later RLHF. Good...
RLHF in Practice: From Human Preferences to Better LLM Policies
TLDR: Reinforcement Learning from Human Feedback (RLHF) helps align language models with human preferences after pretraining and SFT. The typical pipeline is: collect preference comparisons, train a reward model, then optimize a policy (often with KL...
PEFT, LoRA, and QLoRA: A Practical Guide to Efficient LLM Fine-Tuning
TLDR: Full fine-tuning updates every model weight, which is expensive in memory, compute, and storage. PEFT methods update only a small trainable slice. LoRA learns low-rank adapters on top of frozen base weights. QLoRA pushes efficiency further by q...
LLM Model Naming Conventions: How to Read Names and Why They Matter
TLDR: LLM names encode practical decisions: model family, size, training stage, context window, format, and quantization level. If you can decode naming conventions, you can avoid costly deployment mistakes and choose the right checkpoint faster. οΏ½...
Unlocking the Power of ML, DL, and LLM Through Real-World Use Cases
TLDR: ML, Deep Learning, and LLMs are not competing technologies β they are a nested hierarchy. LLMs are a type of Deep Learning. Deep Learning is a subset of ML. Choosing the right layer depends on your data type, problem complexity, and available t...
Text Decoding Strategies: Greedy, Beam Search, and Sampling
TLDR: An LLM doesn't "write" text β it generates a probability distribution over all possible next tokens and then uses a decoding strategy to pick one. Greedy, Beam Search, and Sampling are different rules for that choice. Temperature controls the c...
RLHF Explained: How We Teach AI to Be Nice
TLDR: A raw LLM is a super-smart parrot that read the entire internet β including its worst parts. RLHF (Reinforcement Learning from Human Feedback) is the training pipeline that transforms it from a pattern-matching engine into an assistant that is ...
Mastering Prompt Templates: System, User, and Assistant Roles with LangChain
TLDR: A production prompt is not a string β it is a structured message list with system, user, and optional assistant roles. LangChain's ChatPromptTemplate turns this structure into a reusable, testable, injection-safe blueprint. TLDR: LangChain p...
Prompt Engineering Guide: From Zero-Shot to Chain-of-Thought
TLDR: Prompt Engineering is the art of writing instructions that guide an LLM toward the answer you want. Zero-Shot, Few-Shot, and Chain-of-Thought are systematic techniques β not guesswork β that can dramatically improve accuracy without changing a ...
Multistep AI Agents: The Power of Planning
TLDR: A simple ReAct agent reacts one tool call at a time. A multistep agent plans a complete task decomposition upfront, then executes each step sequentially β handling complex goals that require 5-10 interdependent actions without re-prompting the ...
LoRA Explained: How to Fine-Tune LLMs on a Budget
TLDR: Fine-tuning a 7B-parameter LLM updates billions of weights and requires expensive GPUs. LoRA (Low-Rank Adaptation) freezes the original weights and trains only tiny adapter matrices that are added on top. 90%+ memory reduction; zero inference l...
How to Develop Apps Using LangChain and LLMs
TLDR: LangChain is a framework that simplifies building LLM applications. It provides abstractions for Chains (linking steps), Memory (remembering chat history), and Agents (using tools). It turns raw API calls into composable building blocks. TLD...
Guide to Using RAG with LangChain and ChromaDB/FAISS
TLDR: RAG (Retrieval-Augmented Generation) gives an LLM access to your private documents at query time. You chunk and embed documents into a vector store (ChromaDB or FAISS), retrieve the relevant chunks at query time, and inject them into the LLM's ...
'The Developer''s Guide: When to Use Code, ML, LLMs, or Agents'
TLDR: AI is a tool, not a religion. Use Code for deterministic logic (banking, math). Use Traditional ML for structured predictions (fraud, recommendations). Use LLMs for unstructured text (summarization, chat). Use Agents only when a task genuinely ...
AI Agents Explained: When LLMs Start Using Tools
TLDR: A standard LLM is a brain in a jar β it can reason but cannot act. An AI Agent connects that brain to tools (web search, code execution, APIs). Instead of just answering a question, an agent executes a loop of Thought β Action β Observation unt...
A Guide to Pre-training Large Language Models
TLDR: Pre-training is the phase where an LLM learns "Language" and "World Knowledge" by reading petabytes of text. It uses Self-Supervised Learning to predict the next word in a sentence. This creates the "Base Model" which is later fine-tuned. π ...
LLM Model Quantization: Why, When, and How to Deploy Smaller, Faster Models
TLDR: Quantization converts high-precision model weights and activations (FP16/FP32) into lower-precision formats (INT8 or INT4) so LLMs run with less memory, lower latency, and lower cost. The key is choosing the right quantization method for your a...
LLM Hyperparameters Guide: Temperature, Top-P, and Top-K Explained
TLDR: Temperature, Top-p, and Top-k are three sampling controls that determine how "creative" or "deterministic" an LLM's output is. Temperature rescales the probability distribution; Top-k limits the candidate pool by count; Top-p limits it by cumul...
Mastering Prompt Templates: System, User, and Assistant Roles with LangChain
TLDR: Prompt templates are the contract between your application and the LLM. Role-based messages (System / User / Assistant) provide structure. LangChain's ChatPromptTemplate and MessagesPlaceholder turn ad-hoc strings into versioned, testable pipel...
Tokenization Explained: How LLMs Understand Text
TLDR: LLMs don't read words β they read tokens. A token is roughly 4 characters. Byte Pair Encoding (BPE) builds an efficient subword vocabulary by iteratively merging frequent character pairs. Tokenization choices directly affect cost, context limit...
RAG Explained: How to Give Your LLM a Brain Upgrade
TLDR: LLMs have a training cut-off and no access to private data. RAG (Retrieval-Augmented Generation) solves both problems by retrieving relevant documents from an external store and injecting them into the prompt before generation. No retraining re...
LLM Terms You Should Know: A Helpful Glossary
TLDR: The world of LLMs has its own dense vocabulary. This post is your decoder ring β covering foundation terms (tokens, context window), generation settings (temperature, top-p), safety concepts (hallucination, grounding), and architecture terms (a...
Large Language Models (LLMs): The Generative AI Revolution
TLDR: Large Language Models predict the next token, one at a time, using a Transformer architecture trained on billions of words. At scale, this simple objective produces emergent reasoning, coding, and world-model capabilities. Understanding the tra...
LangChain Tools and Agents: The Classic Agent Loop
π― 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 β Thought β Action β Observation β 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 β swap OpenAI for Anthropic or Ollama by changing one line without touching the rest of your code. π 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. π 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. π 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....
The ReAct Agent Pattern in LangGraph: Think, Act, Observe, Repeat
TLDR: ReAct = Think + Act + Observe, looped as a LangGraph graph β prebuilt or custom. π The Single-Shot Failure: Why One LLM Call Isn't Enough for Complex Tasks Your agent is supposed to write a function, run the tests, fix the failures, and re...
Multi-Agent Systems in LangGraph: Supervisor Pattern, Handoffs, and Agent Networks
TLDR: Split work across specialist agents β supervisor routing beats one overloaded generalist every time. π The Context Ceiling: Why One Agent Can't Do Everything Your research agent is writing a 20-page report. It has 15 tools. Its context windo...
LangGraph Memory and State Persistence: Checkpointers, Threads, and Cross-Session Memory
TLDR: Checkpointers + thread IDs give LangGraph agents persistent memory across turns and sessions. π The Amnesia Problem: Why Stateless Agents Frustrate Users Your customer support agent is on its third message with a user. The user says: "As I ...
Human-in-the-Loop Workflows with LangGraph: Interrupts, Approvals, and Async Execution
TLDR: Pause LangGraph agents mid-run with interrupt(), get human approval, resume with Command. π The Autonomous Agent Risk: When Acting Without Permission Goes Wrong Your autonomous coding agent refactored the authentication module while you were...
LangGraph 101: Building Your First Stateful Agent
TLDR: LangGraph adds state, branching, and loops to LLM chains β build stateful agents with graphs, nodes, and typed state. π The Stateless Chain Problem: Why Your Agent Forgets Everything You built a LangChain chain that answers questions. Then y...
Step-by-Step: How to Expose a Skill as an MCP Server
TLDR: Turn any Python function into a multi-client MCP server in 11 steps β from annotation to Docker. π The Copy-Paste Problem: Why Skills Die at IDE Boundaries A developer pastes their summarize_pr_diff function into a Slack message because thei...
Headless Agents: How to Deploy Your Skills as an MCP Server
TLDR: Deploy once, call everywhere: MCP turns Python skills into headless servers any AI client can call. π The Trapped Skill Problem: When Your Best LLM Tool Works Everywhere But Here You spent an afternoon building a beautiful skill inside GitHu...
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...
LLM Observability: Tracing, Logging, and Debugging Production AI Systems
TLDR: π LLM observability is radically different from traditional APMβnon-deterministic outputs, variable token costs, and multi-step reasoning chains require specialized tracing. LangSmith provides native LangChain integration, OpenTelemetry offers...
LLM Evaluation Frameworks: How to Measure Model Quality (RAGAS, DeepEval, TruLens)
TLDR: π 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....
LangChain RAG: Retrieval-Augmented Generation in Practice
β‘ 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 β load β split β embed...
LangChain Memory: Conversation History and Summarization
TLDR: LLMs are stateless β 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...