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books/LLM Design Patterns/summary.en.md

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+# Book Summary: LLM Design Patterns
+* **Author**: Ken Huang
+* **Genre**: AI Engineering and Software Design
+* **Publication Date**: May 2025
+* **Book Link**: https://amazon.com/dp/1836207034
+
+This document summarizes the key lessons and insights extracted from the book.
+I highly recommend reading the original book for the full depth and author's perspective.
+
+## Before You Get Started
+* I summarize key points from useful books to learn and review quickly.
+* Simply click on `Ask AI` links after each section to dive deeper.
+
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+
+## Introduction and Data Preparation
+
+**Summary**: This opening part lays the groundwork for working with large language models, starting with their evolution from statistical methods to transformer-based giants like BERT and GPT. It stresses the value of design patterns as reusable solutions for common challenges in LLM projects, from data quality to scalability. Then it dives into practical data handling: cleaning noisy text, removing duplicates with techniques like shingling and LSH, augmenting datasets through back-translation or synonym swaps while preserving meaning, managing massive corpora with sharding and efficient formats like Parquet, versioning data for reproducibility using tools like DVC, and annotating datasets for tasks like NER or QA with quality checks via inter-annotator agreement.
+
+**Example**: Think of preparing data like prepping ingredients for a complex recipe—if your veggies are dirty or spoiled, the whole dish suffers. Just as you'd wash and chop carefully, the book shows how deduplicating text corpora prevents redundant training, much like avoiding double-counting in a budget.
+
+**Link for More Details**:
+[Ask AI: Introduction%20and%20Data%20Preparation%7CKen%20Huang%7CLLM%20Design%20Patterns](https://alisol.ir/?ai=Introduction%20and%20Data%20Preparation%7CKen%20Huang%7CLLM%20Design%20Patterns)
+
+## Training and Optimization of Large Language Models
+
+**Summary**: Here, the focus shifts to the nuts and bolts of training LLMs, outlining a modular pipeline from data ingestion to optimization with tools like AdamW and gradient clipping. It covers tuning hyperparameters through methods like Bayesian optimization or PBT, applying regularization such as dropout or L2 to avoid overfitting, checkpointing for fault-tolerant training in distributed setups, fine-tuning pre-trained models by freezing layers or using learning rate schedules, and compressing models via pruning (magnitude-based or iterative) and quantization (PTQ or QAT) to balance size and performance.
+
+**Example**: Training an LLM is like coaching a sports team—you need a solid game plan (pipeline), adjustments based on performance (tuning), rules to prevent burnout (regularization), and save points during long practices (checkpointing). For instance, pruning is akin to trimming a bush: remove the weak branches to let the strong ones thrive without losing the overall shape.
+
+**Link for More Details**:
+[Ask AI: Training%20and%20Optimization%20of%20Large%20Language%20Models%7CKen%20Huang%7CLLM%20Design%20Patterns](https://alisol.ir/?ai=Training%20and%20Optimization%20of%20Large%20Language%20Models%7CKen%20Huang%7CLLM%20Design%20Patterns)
+
+## Evaluation and Interpretation of Large Language Models
+
+**Summary**: This section tackles how to assess and understand LLMs, using benchmarks like MMLU, SuperGLUE, or HumanEval for tasks from reasoning to coding. It discusses cross-validation tailored for pre-training and fine-tuning, interpretability via attention visualization or probing, detecting biases with metrics like demographic parity, boosting robustness against adversarial attacks through training tweaks, and aligning models with human values using RLHF components like reward models and PPO.
+
+**Example**: Evaluating an LLM feels like grading a student's essay—not just checking facts (metrics like perplexity) but understanding their thought process (interpretability via attributions). For bias detection, it's like spotting unfair assumptions in a story; the book explains fixing them without rewriting the whole narrative.
+
+**Link for More Details**:
+[Ask AI: Evaluation%20and%20Interpretation%20of%20Large%20Language%20Models%7CKen%20Huang%7CLLM%20Design%20Patterns](https://alisol.ir/?ai=Evaluation%20and%20Interpretation%20of%20Large%20Language%20Models%7CKen%20Huang%7CLLM%20Design%20Patterns)
+
+## Advanced Prompt Engineering Techniques
+
+**Summary**: Moving into creative prompting, this part explores chain-of-thought for step-by-step reasoning, tree-of-thoughts for branching explorations with search strategies like DFS, ReAct for interleaving thinking and acting (implemented in LangChain), ReWOO for reasoning without direct observations, reflection for self-critique and iterative fixes, and multi-step setups that decompose tasks while auto-selecting tools.
+
+**Example**: Prompting is like giving directions to a friend—CoT is breaking it down turn-by-turn, while ToT is exploring alternate routes if one looks blocked. Imagine troubleshooting a gadget: ReAct lets you think, try a fix, observe, and repeat, turning vague ideas into practical steps.
+
+**Link for More Details**:
+[Ask AI: Advanced%20Prompt%20Engineering%20Techniques%7CKen%20Huang%7CLLM%20Design%20Patterns](https://alisol.ir/?ai=Advanced%20Prompt%20Engineering%20Techniques%7CKen%20Huang%7CLLM%20Design%20Patterns)
+
+## Retrieval and Knowledge Integration in Large Language Models
+
+**Summary**: The final part integrates external knowledge via RAG, building simple systems with embeddings (like SBERT) and indexes (Faiss), advancing to graph-based RAG using embeddings like Node2Vec for structured queries, sophisticated variants with meta-learning or adaptive retrieval, evaluating setups with metrics like MRR or human judgments on benchmarks like NQ, and wrapping up with agentic patterns for goal-driven AI that plans, remembers, decides, and learns ethically.
+
+**Example**: RAG is like consulting a library mid-conversation—instead of relying on memory alone, you pull relevant books (retrieved docs) to enrich your response. Graph RAG adds connections, like a mind map, helping navigate complex topics without getting lost in unrelated facts.
+
+**Link for More Details**:
+[Ask AI: Retrieval%20and%20Knowledge%20Integration%20in%20Large%20Language%20Models%7CKen%20Huang%7CLLM%20Design%20Patterns](https://alisol.ir/?ai=Retrieval%20and%20Knowledge%20Integration%20in%20Large%20Language%20Models%7CKen%20Huang%7CLLM%20Design%20Patterns)
+
+---
+**About the summarizer**
+
+I'm *Ali Sol*, a Backend Developer. Learn more:
+* Website: [alisol.ir](https://alisol.ir)
+* LinkedIn: [linkedin.com/in/alisolphp](https://www.linkedin.com/in/alisolphp)