48 posts · page 1 of 1
Master chain-of-thought prompting to improve LLM accuracy on complex tasks through step-by-step reasoning, zero-shot CoT, and structured thinking patterns.
Learn how to measure prompt quality with evaluation datasets, scoring rubrics, A/B testing, and automated grading to iterate on prompts with evidence.
Master few-shot prompting to teach LLMs new tasks through carefully selected examples, formatting patterns, and example ordering strategies.
Learn prompt patterns for writing, reviewing, debugging, and refactoring code with LLMs, including practical templates and real examples.
Learn how to design multi-turn LLM conversations with effective context management, memory patterns, conversation state tracking, and production architectures.
Learn how to craft system prompts that reliably control LLM behavior through persona setting, constraints, output rules, and guardrails.
Learn how to reliably extract JSON, XML, tables, and other structured formats from LLMs using schema enforcement, prompt patterns, and validation.
Understand how temperature, top-p, max tokens, frequency penalty, and other LLM parameters affect output quality, and learn how to tune them for different tasks.
Learn how to build AI agents with tool use, memory systems, and planning capabilities using Python and LLMs for autonomous task completion.
Understand how AI embeddings work, from text to dense vector representations. Learn to generate, compare, and use embeddings for search, clustering, and classification.
Implement AI guardrails for input validation, output filtering, content moderation, and hallucination prevention to build production-safe LLM applications.
Compare function calling implementations across OpenAI, Anthropic, and Google, with patterns for routing, chaining, and error handling.
Reduce latency and cost of AI model inference with batching, quantization, caching, and request routing strategies.
Design multi-agent systems with supervisor, sequential, and graph-based orchestration patterns for complex LLM workflows.
Build evaluation pipelines for LLM applications with automated metrics, LLM-as-judge patterns, and custom benchmark suites.
Learn how to break complex AI tasks into sequential prompt chains that improve accuracy, debuggability, and output quality.
Understand core AI safety concepts and practical alignment techniques that every developer building AI systems should know.
Reduce LLM API costs and latency by caching responses based on semantic similarity rather than exact string matching.
Extract structured data from unstructured text using LLMs with JSON mode, Pydantic validation, and provider-specific structured output APIs.
Use large language models to generate high-quality synthetic datasets for training, testing, and evaluating AI systems.
Understand the difference between AI agents and AI pipelines, when to choose each, and how to design systems that combine both for reliability and flexibility.
A practical overview of evaluation frameworks for AI applications: what they measure, how they differ, and how to pick one that matches your workflow.
How to design guardrails and content filters for AI applications, including input checks, output checks, layered defenses, and trade-offs between safety and usefulness.
How Stable Diffusion turns text prompts into images: the latent diffusion architecture, sampling loop, and the practical knobs that shape what you get.
How knowledge distillation lets a small student model learn from a large teacher model, why it works, and how to use it to ship smaller, faster models in production.
An introduction to multimodal AI models that handle text, images, audio, and video, including how they work, how to use them, and where they shine.
A practical comparison of leading open source language models: Llama, Mistral, Qwen, Gemma, and Phi families, with guidance on licenses, sizes, and where each fits.
A practical tour of modern recommendation systems: collaborative filtering, content-based methods, hybrid stacks, and how AI ranking models fit on top of candidate generation pipelines.
How RLHF turns raw language models into helpful assistants: the three-stage pipeline, reward modeling, PPO, and the trade-offs that drive newer alternatives like DPO.
A practical introduction to vector search with FAISS: how indexes work, which index to pick, and how to wire it into a real retrieval pipeline for embeddings.
Why bigger context windows are not always better: cost, attention degradation, retrieval design, and how to architect for long-context tasks.
How LLMOps differs from classical MLOps: evaluation, prompts as code, drift, cost, and the workflows that actually work in production.
How prompt injection attacks work, why simple filters fail, and the layered defenses production LLM systems should deploy.
Practical patterns for building AI agents that use tools well: tool definitions, loops, planning, parallel calls, error handling, and how to keep agents from going off the rails.
Build a clear intuition for self-attention: queries, keys, values, softmax weights, and why this single operation lets transformers handle language so well.
What embeddings are, why they work, how to use them for search and clustering, how to pick a model, and the practical pitfalls that bite first-time users.
A practical guide to deciding whether to fine-tune an LLM, choosing between full fine-tuning, LoRA, and instruction tuning, and avoiding the common pitfalls that waste a budget.
A practical comparison of LangChain and LlamaIndex: what each framework is good at, where they overlap, and how to pick one (or skip both) for your LLM application.
A grounded tour of prompt engineering techniques: clear instructions, structured output, few-shot examples, chain-of-thought, role prompts, and evaluation habits that keep prompts honest.
Practical retrieval strategies for RAG: chunking, hybrid search, reranking, query rewriting, metadata filtering, and evaluation patterns that actually move the needle.
Walk through the transformer architecture that powers modern LLMs: tokens, embeddings, self-attention, multi-head attention, feed-forward layers, residuals, and the path from input to output.
A grounded comparison of vector databases for RAG and semantic search: pgvector, Pinecone, Weaviate, Qdrant, Milvus, and Chroma, with guidance on when each shines.
What an embedding is, why cosine similarity works, how dimensionality and chunking choices affect retrieval, and a tiny numpy example you can run in your head.
Why a normal database struggles with vector search, how ANN indexes like HNSW and IVF work, and a clear comparison of pgvector, Qdrant, Pinecone, Chroma, and Weaviate so you can pick one.
Why vibes do not scale: building golden datasets, exact-match vs LLM-as-judge scoring, A/B comparing prompts and models, regression suites, and the observability you need to ship safely.
How LLMs call functions: defining tools with JSON schema, the request → tool-call → response loop, common patterns like search and database queries, and the failure modes that bite in production.
A clear introduction to machine learning for developers — supervised vs unsupervised, features and labels, train/test split, when ML beats rules, and a tiny scikit-learn example.
A clear, honest introduction to large language models — tokens, next-token prediction, training vs inference, context windows, why hallucinations happen, and when LLMs are the right tool.