book

Designing Large Language Model Applications

by Suhas Pai

March 2025

Intermediate to advanced

366 pages

9h 31m

English

O'Reilly Media, Inc.

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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
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
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
VocabularyTokenizersTokenization PipelineNormalizationPre-TokenizationTokenizationByte Pair EncodingWordPieceSpecial TokensSummary
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
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
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
Continual Pre-TrainingReplay (Memory)Parameter ExpansionParameter-Efficient Fine-TuningAdding New ParametersSubset MethodsCombining Multiple ModelsModel EnsemblingModel FusionAdapter MergingSummary

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
LLM Inference ChallengesInference Optimization TechniquesTechniques for Reducing ComputeK-V CachingEarly ExitKnowledge DistillationTechniques for Accelerating DecodingSpeculative DecodingParallel DecodingTechniques for Reducing Storage NeedsSymmetric QuantizationAsymmetric QuantizationSummary
LLM Interaction ParadigmsPassive ApproachThe Explicit ApproachThe Autonomous ApproachDefining AgentsAgentic WorkflowComponents of an Agentic SystemModelsToolsData StoresAgent Loop PromptGuardrails and VerifiersAgent Orchestration SoftwareSummary
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
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
Multi-LLM ArchitecturesLLM CascadesRoutersTask-Specialized LLMsProgramming ParadigmsDSPyLMQLSummary

Content preview from Designing Large Language Model Applications

Chapter 10. Interfacing LLMs with External Tools

In the first two parts of the book, we have seen how impactful standalone LLMs can be in solving a wide variety of tasks. To effectively harness their full range of capabilities in an organization, they have to be integrated into the existing data and software ecosystem. Unlike traditional software systems, LLMs can generate autonomous actions to interact with other ecosystem components, bringing a degree of flexibility never seen before in the software world. This flexibility unlocks a whole host of use cases that were previously considered impossible.

Another reason we need LLMs to interact with software and external data: as we know all too well, current LLMs have significant limitations, some of which we discussed in Chapter 1. To recap some key points:

Since it is expensive to retrain LLMs or keep them continuously updated, they have a knowledge cutoff date and thus possess no knowledge of more recent events.
Even though they are getting better over time, LLMs don’t always get math right.
They can’t provide factuality guarantees or accurately cite the sources of their outputs.
Feeding them your own data effectively is a challenge; fine-tuning is nontrivial, and in-context learning is limited by the length of the effective context window.

As we have been noticing throughout the book, the consolidation effect is leading us to a future (unless we hit a technological wall) where many of the aforementioned limitations ...