65 posts · page 1 of 2
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 practical strategies for handling long documents within LLM context windows, including chunking, summarization, sliding windows, and map-reduce patterns.
Master LLM evaluation with automated metrics like BLEU and ROUGE, plus human evaluation frameworks for measuring quality, safety, and reliability.
Build a production-ready RAG pipeline with document ingestion, chunking, retrieval, and generation. Covers error handling, monitoring, and optimization.
Master prompt engineering techniques for generating, debugging, and refactoring code with LLMs. Includes practical patterns, templates, and Python examples for developers.
Learn how to prompt LLMs to return structured JSON output reliably. Covers schema enforcement, Pydantic validation, and production patterns for consistent structured responses.
Compare function calling implementations across OpenAI, Anthropic, and Google, with patterns for routing, chaining, and error handling.
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.
A hands-on guide to fine-tuning large language models using LoRA, QLoRA, and Hugging Face. Covers dataset preparation, training configuration, evaluation, and deployment considerations.
How to implement function calling and tool use with LLMs. Covers tool definitions, the execution loop, multi-turn conversations, error handling, and parallel tool calls.
How to reliably get JSON, typed objects, and structured data from LLMs using JSON mode, function calling, Pydantic schemas, and constrained generation with the Outlines library.
How LLMs break text into tokens using BPE, SentencePiece, and tiktoken. Covers vocabulary construction, token limits, and practical implications for prompt engineering.
A practical overview of evaluation frameworks for AI applications: what they measure, how they differ, and how to pick one that matches your workflow.
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.
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.
Why bigger context windows are not always better: cost, attention degradation, retrieval design, and how to architect for long-context tasks.
How prompt injection attacks work, why simple filters fail, and the layered defenses production LLM systems should deploy.
A practical guide to attributing, monitoring, and controlling LLM spend per user, per feature, and per request without slowing down delivery.
Decide between prompt engineering, retrieval, and fine-tuning by weighing cost, latency, control, and data requirements honestly.
How to design tool schemas that LLMs actually call correctly, with naming, description, and parameter patterns that survive real users and adversarial inputs.
An engineering-focused comparison of Grok, Claude, and GPT model families across reasoning, tool use, context, latency, and real production trade-offs.
RAG and grounding are often used interchangeably but they describe different techniques. Here is how to tell them apart and when each one matters.
Practical defenses against prompt injection, role hijacking, and policy bypasses in production LLM systems, with layered controls that actually work.
How to design multi-turn LLM conversations that stay coherent, respect context limits, handle long histories, and support useful features like summarization and recall.
Practical techniques for parsing and validating LLM outputs reliably, covering JSON mode, schema enforcement, retries, and repair strategies for production use.
How prompt caching works in modern LLM APIs, when it saves significant cost and latency, and how to design prompts so the cache actually hits in production.
How quantization shrinks LLMs to run on smaller hardware, the math behind 8-bit and 4-bit weights, and the trade-offs between speed, memory, and quality.
How to handle provider rate limits, transient failures, and quota exhaustion in production LLM apps with backoff, queues, and graceful degradation.
A practical guide to self-hosting open-source language models using vLLM, covering setup, batching, and serving for production workloads.
Stream tokens from an LLM as they are generated to cut perceived latency, handle partial outputs, and build responsive chat UIs.
Understand how temperature and top-p sampling shape the creativity, determinism, and quality of large language model outputs.
Learn how tokens are counted, how to estimate API spend before you send a request, and concrete strategies to cut LLM bills without hurting quality.
Understand how tool calling lets LLMs invoke functions, why agents loop over tools, and how to design reliable tool schemas.
How to get reliable JSON out of LLMs using tool use, JSON mode, and grammar-constrained decoding, with patterns that work in production.
A field guide to the most common prompt engineering anti-patterns, why they degrade LLM output quality, and concrete refactors that fix each one.
How to build evaluation loops for prompts so you can iterate with evidence instead of vibes. Covers datasets, graders, regressions, and how to make eval cheap enough to run often.
How to coax LLMs into producing predictable, parseable output using output formatters, JSON schemas, examples, and validation loops that actually hold up in production code paths.
Decide between zero-shot and few-shot prompting by weighing example quality, cost, and how strictly you need to control output format.
Learn the ReAct pattern, a prompting technique that combines reasoning and action to build effective tool-using LLM agents.