ai

How to design AI agents that autonomously plan, execute tools, and iterate toward goals using ReAct, function calling, and memory architectures.

Build autonomous AI agents that can use external tools and APIs to accomplish complex tasks.

How to create an AI chatbot using the OpenAI Assistants API with function calling and file retrieval

How to create, edit, and optimize images using DALL-E, Stable Diffusion, and Midjourney APIs with prompt engineering, batch processing, and content moderation.

How to fine-tune a large language model for domain-specific code generation using LoRA, QLoRA, and custom datasets

How to write useful prompts for LLMs using role assignment, few-shot examples, chain-of-thought reasoning, and structured output formatting.

Create multi-step AI agents with LangGraph using state machines, conditional edges, tool calling, and human-in-the-loop checkpoints for production workflows

Fine-tune a pre-trained transformer model for text classification using Hugging Face Trainer, tokenize datasets, evaluate metrics, and deploy for inference

Build composable LLM pipelines with LangChain Expression Language (LCEL) using pipes, parallel execution, and custom runnable components

Measure RAG pipeline quality using RAGAS framework metrics — faithfulness, answer relevancy, context precision, and context recall for objective evaluation

Stream LLM responses to clients in real-time using Server-Sent Events with FastAPI, OpenAI streaming, and async generators for token-by-token output

Install and use Ollama to run open-source LLMs locally with Python, including streaming, embeddings, function calling, and model management without API costs

Generate text embeddings with OpenAI and compute cosine similarity to measure semantic similarity between texts for search, dedup, and clustering

Use OpenAI function calling and structured outputs to get reliable JSON from LLMs with Pydantic validation and error handling

Implement retrieval-augmented generation locally with ChromaDB, sentence-transformers embeddings, and LLM generation without external API dependencies

Score text sentiment using NLTK VADER and custom lexicons in Python.

Use Pinecone to store, query, and filter vector embeddings for semantic search with metadata filtering and namespace isolation

How to build a Retrieval-Augmented Generation (RAG) pipeline using LangChain and vector databases for AI-powered search

How to implement semantic search using text embeddings and vector similarity search for intelligent document retrieval

How to build a conversational Slack bot powered by OpenAI GPT-4 that responds to mentions and direct messages

Dynamically select which tools an LLM agent can use based on the task context. Reduce token usage and improve decision quality by narrowing the tool set.

Cache LLM embeddings to reduce API calls and cost. Store embeddings with a content hash key and serve from cache on repeated inputs.

Pause LLM agent execution for human approval before high-impact actions. Route decisions to a reviewer when confidence is low or stakes are high.

Fall back to alternative LLM providers or models when the primary fails. Handle rate limits, timeouts, and errors gracefully with a provider chain.

Validate LLM inputs and outputs with rules, classifiers, and content filters. Prevent prompt injection, toxic content, and data leakage before reaching users.

Route queries to different LLM models based on complexity, cost, and latency requirements. Classify input before dispatching to the right model.

Chain multiple LLM calls where each step's output feeds the next step's input. Break complex tasks into smaller, verifiable prompts for better results.

Combine keyword (BM25) and semantic (vector) search to improve retrieval accuracy in RAG pipelines. Fuse ranked results using reciprocal rank fusion.

Document AI agent architecture, tools, memory, reasoning patterns, safety guardrails, evaluation criteria, and deployment configuration. Includes sections for system prompts, tool definitions, and failure modes.

Checklist for preparing data for LLM and RAG systems: data collection, cleaning, chunking, embedding, deduplication, PII removal, format validation, quality scoring, and indexing with metrics and thresholds.

Track token usage and costs per feature, model, and user. Includes cost categories, pricing tables, budget alerts, optimization strategies, and reporting templates for LLM API spending.

Operational runbook for LLM production incidents: hallucination events, model outages, cost spikes, safety failures, and degraded quality. Includes severity levels, escalation paths, diagnostic steps, and recovery procedures.

A reusable prompt template library for common LLM tasks: summarization, extraction, classification, code review, translation, and structured output with variables, examples, and evaluation criteria.

Compare LLM models by cost, latency, context window, accuracy, and use case. Includes decision criteria, benchmark results, pricing comparison, and recommendations for classification, extraction, summarization, code, and agent tasks.

Version your LLM prompts with eval scores, change history, rollback support, and A/B testing. Includes prompt metadata schema, changelog format, evaluation tracking, and CI/CD integration for prompt management.

A checklist for evaluating RAG system quality: retrieval accuracy, generation faithfulness, context relevance, answer correctness, citation accuracy, latency, and end-to-end testing with metrics and thresholds.

Build production AI agents. Covers agent architectures, tool use, planning, memory, multi-agent systems, ReAct patterns, function calling, human-in-the-loop, safety, and deployment patterns for reliable autonomous agents.

Run LangChain in production. Covers chains, agents, memory, tools, LCEL, streaming, callbacks, RAG integration, evaluation, and deployment patterns for reliable LangChain-powered applications.

Build production LLM applications end-to-end. Covers API layers, prompt management, streaming, caching, guardrails, observability, evaluation, and deployment patterns for reliable LLM-powered systems.

Optimize LLM costs in production. Covers model routing, prompt compression, caching, batch API, token management, semantic caching, prompt engineering for cost, monitoring, and budget control patterns for LLM applications.

Evaluate LLM applications in production. Covers RAGAS, LLM-as-judge, human evaluation, A/B testing, hallucination detection, toxicity scoring, regression testing, and building automated evaluation pipelines.

Write effective prompts for AI models. Covers prompt patterns, few-shot learning, chain-of-thought, RAG, system prompts, temperature tuning, function calling, and evaluation strategies.

Secure LLM applications in production. Covers prompt injection, jailbreaks, data leakage, OWASP Top 10 for LLMs, input validation, output filtering, rate limiting, red teaming, and building secure LLM pipelines with guardrails.

Deploy LLMs locally and on-premise. Covers Ollama, vLLM, llama.cpp, LM Studio, model quantization, GPU requirements, serving with API servers, performance tuning, and choosing between local and cloud LLM deployment.

Master the OpenAI API in production. Covers chat completions, streaming, function calling, structured outputs, embeddings, fine-tuning, batch API, assistants API, rate limits, error handling, and cost optimization patterns.

Build production RAG systems. Covers chunking strategies, embedding models, vector stores, retrieval optimization, reranking, hybrid search, evaluation, and deployment patterns for reliable retrieval-augmented generation.

Compare and use vector databases in production. Covers Pinecone, Weaviate, Chroma, pgvector, Milvus, and Qdrant. Includes indexing, similarity search, filtering, scaling, benchmarking, and choosing the right vector database.

A practical guide to vector databases: embeddings, similarity search, approximate nearest neighbors, and choosing between Pinecone, Weaviate, pgvector, and Chroma.