book

Designing Large Language Model Applications

Name: Designing Large Language Model Applications
Author: Suhas Pai
ISBN: 9781098150501

by Suhas Pai

March 2025

Intermediate to advanced

366 pages

9h 31m

English

O'Reilly Media, Inc.

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Includes

Quizzes

Preface
Who This Book Is ForHow This Book Is StructuredWhat This Book Is Not AboutHow to Read the BookConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
I. LLM Ingredients
1. Introduction
Defining LLMsA Brief History of LLMsEarly YearsThe Modern LLM EraThe Impact of LLMsLLM Usage in the EnterprisePromptingZero-Shot PromptingFew-Shot PromptingChain-of-Thought PromptingPrompt ChainingAdversarial PromptingAccessing LLMs Through an APIStrengths and Limitations of LLMsBuilding Your First Chatbot PrototypeFrom Prototype to ProductionSummary
2. Pre-Training Data
Ingredients of an LLMPre-Training Data RequirementsPopular Pre-Training DatasetsSynthetic Pre-Training DataTraining Data PreprocessingData Filtering and CleaningSelecting Quality DocumentsDeduplicationRemoving Personally Identifiable InformationTraining Set DecontaminationData MixturesEffect of Pre-Training Data on Downstream TasksBias and Fairness Issues in Pre-Training DatasetsSummary
3. Vocabulary and Tokenization
VocabularyTokenizersTokenization PipelineNormalizationPre-TokenizationTokenizationByte Pair EncodingWordPieceSpecial TokensSummary
4. Architectures and Learning Objectives
PreliminariesRepresenting MeaningThe Transformer ArchitectureSelf-AttentionPositional EncodingFeedforward NetworksLayer NormalizationLoss FunctionsIntrinsic Model EvaluationTransformer BackbonesEncoder-Only ArchitecturesEncoder-Decoder ArchitecturesDecoder-Only ArchitecturesMixture of ExpertsLearning ObjectivesFull Language ModelingPrefix Language ModelingMasked Language ModelingWhich Learning Objectives Are Better?Pre-Training ModelsSummary
II. Utilizing LLMs
5. Adapting LLMs to Your Use Case
Navigating the LLM LandscapeWho Are the LLM providers?Model FlavorsOpen Source LLMsHow to Choose an LLM for Your TaskOpen Source Versus Proprietary LLMsLLM EvaluationLoading LLMsHugging Face AccelerateOllamaLLM Inference APIsDecoding StrategiesGreedy DecodingBeam SearchTop-k SamplingTop-p SamplingRunning Inference on LLMsStructured OutputsModel Debugging and InterpretabilitySummary
6. Fine-Tuning
The Need for Fine-TuningFine-Tuning: A Full ExampleLearning Algorithms ParametersMemory Optimization ParametersRegularization ParametersBatch SizeParameter-Efficient Fine-TuningWorking with Reduced PrecisionPutting It All TogetherFine-Tuning DatasetsUtilizing Publicly Available Instruction-Tuning DatasetsLLM-Generated Instruction-Tuning DatasetsSummary
7. Advanced Fine-Tuning Techniques
Continual Pre-TrainingReplay (Memory)Parameter ExpansionParameter-Efficient Fine-TuningAdding New ParametersSubset MethodsCombining Multiple ModelsModel EnsemblingModel FusionAdapter MergingSummary

8. Alignment Training and Reasoning
Defining Alignment TrainingReinforcement LearningTypes of Human FeedbackRLHF ExampleHallucinationsMitigating HallucinationsSelf-ConsistencyChain-of-ActionsRecitationSampling Methods for Addressing HallucinationDecoding by Contrasting LayersIn-Context HallucinationsHallucinations Due to Irrelevant InformationReasoningDeductive ReasoningInductive ReasoningAbductive ReasoningCommon Sense ReasoningInducing Reasoning in LLMsVerifiers for Improving ReasoningInference-Time ComputationFine-Tuning for ReasoningSummary
9. Inference Optimization
LLM Inference ChallengesInference Optimization TechniquesTechniques for Reducing ComputeK-V CachingEarly ExitKnowledge DistillationTechniques for Accelerating DecodingSpeculative DecodingParallel DecodingTechniques for Reducing Storage NeedsSymmetric QuantizationAsymmetric QuantizationSummary
III. LLM Application Paradigms
10. Interfacing LLMs with External Tools
LLM Interaction ParadigmsPassive ApproachThe Explicit ApproachThe Autonomous ApproachDefining AgentsAgentic WorkflowComponents of an Agentic SystemModelsToolsData StoresAgent Loop PromptGuardrails and VerifiersAgent Orchestration SoftwareSummary
11. Representation Learning and Embeddings
Introduction to EmbeddingsSemantic SearchSimilarity MeasuresFine-Tuning Embedding ModelsBase ModelsTraining DatasetLoss FunctionsInstruction EmbeddingsOptimizing Embedding SizeMatryoshka EmbeddingsBinary and Integer EmbeddingsProduct QuantizationChunkingSliding Window ChunkingMetadata-Aware ChunkingLayout-Aware ChunkingSemantic ChunkingLate ChunkingVector DatabasesInterpreting EmbeddingsSummary
12. Retrieval-Augmented Generation
The Need for RAGTypical RAG ScenariosDeciding When to RetrieveThe RAG PipelineRewriteRetrieveRerankRefineInsertGenerateRAG for Memory ManagementRAG for Selecting In-Context Training ExamplesRAG for Model TrainingLimitations of RAGRAG Versus Long ContextRAG Versus Fine-TuningSummary
13. Design Patterns and System Architecture
Multi-LLM ArchitecturesLLM CascadesRoutersTask-Specialized LLMsProgramming ParadigmsDSPyLMQLSummary
Index
About the Author

Content preview from Designing Large Language Model Applications

Part I. LLM Ingredients

We begin this book by introducing large language models (LLMs) and the key ingredients that go into making them. This includes understanding how training data is collected and prepared, examining the model’s vocabulary, and exploring the architectures underpinning them.

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Designing Large Language Model Applications

by Suhas Pai

Part I. LLM Ingredients

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.