PacktPublishing/LLM-Engineers-Handbook

Features

• Retrieval-Augmented Generation - Provides a comprehensive framework for building retrieval-augmented generation systems to ground model outputs in external data.
• LLM Engineering Guides - Serves as a comprehensive engineering guide for building, deploying, and optimizing large language model applications.
• LLM Inference Servers - Deploys production-ready servers specifically designed for hosting and serving large language model inference.
• RAG Pipelines - Implements workflows that retrieve and integrate external data from document sources to augment model outputs.
• LLM Evaluation Frameworks - Includes a suite of methods and scripts to measure model accuracy and performance through systematic experiments.
• LLM Training Orchestrators - Coordinates the orchestration of data pipelines for crawling and processing datasets used in LLM fine-tuning.
• Model Fine-Tuning - Coordinates data preparation and fine-tuning processes to adapt pre-trained models to specific tasks.
• Model Training Pipelines - Implements end-to-end workflows for crawling, structuring, and processing datasets to facilitate model fine-tuning.
• Training and Evaluation Pipelines - Executes automated workflows for model training, epoch iteration, and validation within cloud environments.
• Model Performance Evaluators - Quantifies the accuracy and reliability of models through systematic testing to ensure production readiness.
• Modular Training Architectures - Coordinates modular pipelines for data extraction, cleaning, and fine-tuning to improve model performance.
• RAG Context Retrieval - Implements retrieval of relevant document segments from knowledge bases to ground LLM responses.
• RAG Frameworks - Provides a framework of patterns and practices for integrating external data retrieval into LLM inference pipelines.
• RAG System Design - Offers architectural patterns and design guides for building holistic retrieval-augmented generation systems.
• Model Evaluation and Benchmarking - Provides frameworks and suites for testing, validating, and comparing the performance of language models.
• Model Endpoint Deployment - Provides a blueprint for provisioning cloud infrastructure to host AI models as reachable API endpoints.
• Cloud Orchestration - Manages the end-to-end lifecycle of AI solutions from data warehousing to scalable cloud resource orchestration.
• Training Data Generation - Creates and curates datasets from web content to improve the quality and diversity of model training.
• AI Cloud Infrastructure - Provides guidelines and a blueprint for training, serving, and scaling AI models using cloud infrastructure.
• Deployment Solutions - Orchestrates end-to-end cloud infrastructure and containerization for scaling production AI environments.
• Prompt Execution Tracing - Tracks and analyzes the execution flow of model queries to optimize prompt engineering and inspect behavior.
• Prompt Flow Monitoring - Tracks and analyzes the execution flow of prompts to monitor and inspect model behavior.
• AI Inference Infrastructure - Offers a blueprint for deploying AI model serving infrastructure, including inference endpoints and production observability.
• Training Dataset Generators - Includes workflows for crawling and structuring web content into datasets for model training and evaluation.

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