Docs

This document defines LangChain and explores its core components including language models, chat models, chat messages, prompt templates, example selectors, and output parsers for building LLM applications.

This document introduces LangChain Expression Language (LCEL), covering how to build flexible chains using the pipe operator, structure prompts with templates, and develop reusable patterns for AI applications.

This document explores advanced prompt engineering methods including zero-shot, few-shot, chain-of-thought, and self-consistency techniques, along with practical tools and applications for effective LLM interactions.

This document introduces LangChain, an open-source Python framework for developing LLM applications, exploring its benefits, practical uses, and integration capabilities with various data types.

This document introduces in-context learning and prompt engineering explaining how LLMs can learn new tasks from examples provided in prompts without additional training, along with techniques for crafting effective prompts to guide AI systems.

This document provides a comprehensive reference guide to generative AI covering fundamental concepts, key models, applications, and best practices for implementing GenAI solutions.

This document introduces natural language processing, explaining how computers translate between unstructured human language and structured data through techniques like tokenization, stemming, lemmatization, part of speech tagging and named entity recognition.

This document explores foundation models and large language models, covering their training methodology, advantages in performance and productivity, as well as challenges related to compute costs and trustworthiness in enterprise applications.

This document introduces generative AI, its evolution from discriminative AI and the foundational models that enable creative content generation across text, images, video, and code.

This document covers Python implementations (CPython, PyPy, Jython) development environments, and IDE comparisons. It provides guidance on choosing and setting up the right tools for Python development and data science work.

This document covers postmortem documentation for incident response, including purpose, structure, essential components like root cause and prevention measures, focusing on learning rather than blame, and practicing postmortem writing for continuous improvement. Learning from incidents through documentation.

This document covers communication and documentation strategies during incident response, including tracking troubleshooting activities communicating with affected users, coordinating team roles like incident commander and communications lead, and creating effective post-incident summaries. Incident management best practices.

This document covers debugging techniques for complex multi-service systems including log analysis across distributed services, identifying service dependencies, rollback strategies, load balancer troubleshooting, and infrastructure management for cloud-based applications. Distributed system debugging strategies.

This document covers AI-infused debugging and paired programming techniques including AI copilot tools like Google Gemini, GitHub Copilot, ChatGPT collaborative debugging workflows, paired programming practices, and best practices for using AI assistants. AI-powered development assistance.

This document covers additional debugging techniques including IDE breakpoints, Visual Studio Code debugger features, conditional breakpoints variable inspection, and comparing IDE debugging with command-line approaches. IDE-based debugging strategies.

This document covers debugging Python programs using PDB interactive debugger including setting breakpoints, stepping through code, inspecting and modifying variables, and post-mortem debugging. Python's built-in interactive debugger.

This document covers debugging Python programs using the logging module including log levels, configuration, file output, custom formatters, and best practices for production-grade logging. Professional debugging and monitoring technique.

This document covers debugging Python programs using try-except blocks for exception handling, including catching specific exceptions, custom exceptions finally clauses, and best practices for graceful error handling. Essential exception handling technique.

This document covers debugging Python programs using assert statements including assertion syntax, sanity checks, precondition validation, and best practices for catching bugs early in development. Proactive bug detection technique.

This document covers debugging Python programs using print statements including strategies for variable inspection, execution flow tracking formatted output techniques, and best practices for effective printf debugging. Simple yet powerful debugging technique.