Notes from a personal journal.

Building Your First Agent Harness

An e-commerce customer-service chatbot in about a hundred lines of Python: four tools, three hook layers, and the ratchet of what to add only when you watch it fail.

Elevating Your AI with Advanced RAG Techniques : A Comprehensive Guide

Retrieval-Augmented Generation (RAG) has revolutionized how AI applications interact with vast knowledge bases, enabling models to generate more accurate and contextually relevant responses.

21 minute read

️ Understanding RAG — From Memory to Real-Time Retrieval

In today’s AI landscape, language models are like students who have read many books but still need to look up information for precise answers.

Real-World Power: Advanced Applications of LangChain & LlamaIndex in AI Solutions

In this chapter, we dive into advanced real-world applications of the powerful frameworks LangChain and LlamaIndex.

🏛️ From Attention to Advanced AI: Decoding Modern LLMs with Transformers, LLaMA, GPT, and More…

In this chapter, we’ll dive into the evolution of Large Language Models (LLMs), starting from the basics of transformers to the rise of popular models like LLaMA, GPT, and Claude.

🏗️ Introduction to LLMs: Understanding the Building Blocks of AI 📝🌐

If you’ve heard of AI models like GPT, you might wonder why they’re so good at understanding and responding to our questions.

Building Your First Agent Harness

An e-commerce customer-service chatbot in about a hundred lines of Python: four tools, three hook layers, and the ratchet of what to add only when you watch it fail.

Elevating Your AI with Advanced RAG Techniques : A Comprehensive Guide

Retrieval-Augmented Generation (RAG) has revolutionized how AI applications interact with vast knowledge bases, enabling models to generate more accurate and contextually relevant responses.

21 minute read

️ Understanding RAG — From Memory to Real-Time Retrieval

In today’s AI landscape, language models are like students who have read many books but still need to look up information for precise answers.

Real-World Power: Advanced Applications of LangChain & LlamaIndex in AI Solutions

In this chapter, we dive into advanced real-world applications of the powerful frameworks LangChain and LlamaIndex.

🏛️ From Attention to Advanced AI: Decoding Modern LLMs with Transformers, LLaMA, GPT, and More…

In this chapter, we’ll dive into the evolution of Large Language Models (LLMs), starting from the basics of transformers to the rise of popular models like LLaMA, GPT, and Claude.

🏗️ Introduction to LLMs: Understanding the Building Blocks of AI 📝🌐

If you’ve heard of AI models like GPT, you might wonder why they’re so good at understanding and responding to our questions.

Inside the Harness

The six pieces of scaffolding that turn a model into an agent, with examples from coding agents and e-commerce chatbots alike.

️ Modular RAG: Crafting Customizable Knowledge Retrieval Systems

Imagine building a team of experts, each specializing in a unique area, working together seamlessly to solve a complex problem.

Real-World Magic — How RAG Transforms Industries ✨

Retrieval-Augmented Generation (RAG) has emerged as a key AI innovation across industries, offering dynamic, accurate, and context-rich responses.

🗣️ Mastering Prompting: Techniques, Tips, and Security for Effective AI Conversations 💬🔧🛡️

Prompting is the fundamental skill needed to communicate effectively with Large Language Models (LLMs).

🛠️ Mastering LLMs in the Real World: Evaluating Performance, Tackling Hallucinations, Bias, and Boosting Efficiency 📊🚀

While LLMs have revolutionized AI, they still encounter real-world challenges like hallucinations, bias, and compute limitations.

🧠🔓 AI Unlocked: Building and Mastering Large Language Models, Step-by-Step 📚

Welcome to the ultimate series on 🧠🔓AI Unlocked: Building and Mastering Large Language Models, Step-by-Step 📚from scratch!

Inside the Harness

The six pieces of scaffolding that turn a model into an agent, with examples from coding agents and e-commerce chatbots alike.

️ Modular RAG: Crafting Customizable Knowledge Retrieval Systems

Imagine building a team of experts, each specializing in a unique area, working together seamlessly to solve a complex problem.

Real-World Magic — How RAG Transforms Industries ✨

Retrieval-Augmented Generation (RAG) has emerged as a key AI innovation across industries, offering dynamic, accurate, and context-rich responses.

🗣️ Mastering Prompting: Techniques, Tips, and Security for Effective AI Conversations 💬🔧🛡️

Prompting is the fundamental skill needed to communicate effectively with Large Language Models (LLMs).

🛠️ Mastering LLMs in the Real World: Evaluating Performance, Tackling Hallucinations, Bias, and Boosting Efficiency 📊🚀

While LLMs have revolutionized AI, they still encounter real-world challenges like hallucinations, bias, and compute limitations.

🧠🔓 AI Unlocked: Building and Mastering Large Language Models, Step-by-Step 📚

Welcome to the ultimate series on 🧠🔓AI Unlocked: Building and Mastering Large Language Models, Step-by-Step 📚from scratch!

Stop Blaming the Model

Why your AI agent is dumber than it should be, whether it's writing code, answering customers, or taking voice orders, and the discipline that actually fixes it.

8 minute read

️ Fine-Tuning LLMs for Precision: Unlocking the Full Potential of AI

Imagine you’ve trained a chef who can cook any dish. But if you need them to perfect a regional specialty or add a unique twist, you’ll give them more specific training.

10 minute read

️ Mastering LLM Workflows with LangChain & LlamaIndex

LangChain and LlamaIndex are two widely used frameworks in the LLM ecosystem, designed to simplify interactions with LLMs, manage conversation flows, and facilitate efficient data retrieval.

🏛️ LLM Architectures and Landscape: The Journey from Attention to Transformers 🚀📚🔍

In this chapter, we explore the historical development of the attention mechanism and how it led to the creation of transformers, the core architecture of modern Large Language Models (LLMs).

🧠 Why Prompt Engineering, Fine-Tuning, and RAG? 🤔🎯

Have you ever wondered how AI models like GPT get so good at understanding and responding to our questions?

Stop Blaming the Model

Why your AI agent is dumber than it should be, whether it's writing code, answering customers, or taking voice orders, and the discipline that actually fixes it.

8 minute read

️ Fine-Tuning LLMs for Precision: Unlocking the Full Potential of AI

Imagine you’ve trained a chef who can cook any dish. But if you need them to perfect a regional specialty or add a unique twist, you’ll give them more specific training.

10 minute read

️ Mastering LLM Workflows with LangChain & LlamaIndex

LangChain and LlamaIndex are two widely used frameworks in the LLM ecosystem, designed to simplify interactions with LLMs, manage conversation flows, and facilitate efficient data retrieval.

🏛️ LLM Architectures and Landscape: The Journey from Attention to Transformers 🚀📚🔍

In this chapter, we explore the historical development of the attention mechanism and how it led to the creation of transformers, the core architecture of modern Large Language Models (LLMs).

🧠 Why Prompt Engineering, Fine-Tuning, and RAG? 🤔🎯

Have you ever wondered how AI models like GPT get so good at understanding and responding to our questions?