book

Large Language Models: The Hard Parts

by Thársis T. P. Souza, Jonathan K. Regenstein Jr.

May 2026

Intermediate to advanced

340 pages

8h 45m

English

O'Reilly Media, Inc.

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Foreword
Preface
Who Should Read This BookNavigating This Book by ChapterConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
1. First Principles: What to Consider Before We Start Building with LLMs
Why Open Source?Strategic ConsiderationsEnterprise RequirementsStakeholder RequirementsCompliance and Security RequirementsPerformance RequirementsOperational RequirementsIntegration RequirementsData Management RequirementsOrganizational AI FrameworksCentralized FrameworkDecentralized FrameworkFederated FrameworkThe Importance of DataModel TypesBase ModelsInstruction Fine-Tuned ModelsDomain Adapted ModelsModel FeaturesContext LengthOutput ControlCachingOutput Token LengthCost and SpeedLicensingCustomizationSmall Language ModelsConclusion
2. The Evals Gap
Nondeterministic Nature of LLMsSource of NondeterminismTemperature10-K LLMBA Temperature TestsThe Evals ChallengeDesigning an LLMBA Evaluation FrameworkConceptual Design: Single LLMBAConceptual Design: Multiple LLMBAsMethodologies for EvaluationQuantitative MetricsCoding BLEU and ROUGECoding an LLM-as-a-JudgeLimitations of LLM-as-a-JudgeEvaluating EvaluatorsA Brief Tour of LLM BenchmarksARC-AGI and the PrizeConclusion
3. Evaluation Tools for LLM-Based Applications
LangSmithBLEU with LangSmithScaling LLM-as-a-Judge with LangSmithPromptfooEvaluating Models with PromptfooEvaluating PromptsLightEvalLightEval SetupEconometric Evaluation with MMLUComparing Multiple Models on EconometricsFrameworks ComparisonConclusion
4. From Data to Context
Preprocessing DocumentsPyPDF2MarkItDownDoclingRAGRAG ProcessChunking DocumentsChunking StrategiesChunking Case StudyVector EmbeddingsRerankingReport GenerationRAG Challenges and LimitationsLong-Context Models: RAG Killer?LCM Case Study: Quiz Generation with CitationsImplementationExample UsageDiscussion and LimitationsConclusion
5. Structured Data Output
Why Structured Output MattersImproving Developer Efficiency and WorkflowMeeting UI and Product RequirementsEnhancing User Trust and ExperienceWhy Is This Hard? The Transformer ArchitectureTraining-Time Constraint TechniquesInference-Time Constraint TechniquesPrompt EngineeringJSON Mode (Fine-Tuned)Combining JSON Mode with PydanticLogit PostprocessingOutlinesOllamaOllama with PydanticLangChainComparing ToolsConclusion
6. LLM Safety Considerations
General AI Safety RisksLLM-Specific Safety RisksJailbreakingPrompt Injection (Prompt Crafting)Stealth EditingAddressing LLMBA Safety RisksMisinformation PreventionUnqualified Advice PreventionBias DetectionPrivacy ProtectionGuidance from AI Labs and Policy CentersOpenAIAnthropicGoogleMLCommons AI Safety BenchmarkCentre for the Governance of AI RubricTwo Specific Approaches to LLM SafetyRed TeamingConstitutional AIConclusion
7. Evaluating LLMs for Safety
Safety Evaluation with Benchmark DatasetsSALAD-BenchTruthfulQAHarmBenchRuntime Guardrails and ModerationNeMo GuardrailsTruLens GuardrailsLlama GuardMistral’s Moderation APIOpenAI’s Moderation APICustom Moderation with LLM-as-a-JudgeCase Study: Implementing a Safety Filter for K–12 StudentsEvals DatasetSafety FiltersBenchmarkingConclusion
8. LLM Alignment: A Case Study
Why Is Alignment Necessary?RLHF: The Breakthrough That Made LLMs TrustworthyProximal Policy OptimizationDPOCase Study: Aligning an LLM to a PolicyAlignment ToolsAcme’s Educational PolicyPreference Dataset—Synthetic Dataset GenerationFine-TuningAlignment Evaluation: LLM-as-a-JudgeDPO Dataset CompositionOur Choice of Base ModelThe Evaluation MethodologyThe Fine-Tuning Process and Parameter ChoiceDesigning a Safety and Alignment PlanPhase 1: Policy DefinitionPhase 2: User Research and Risk IdentificationPhase 3: Evaluation FrameworkPhase 4: Safety Architecture DesignPhase 5: Implementation and Tools SelectionPhase 6: Incident ResponseCommon PitfallsConclusion

9. Epilogue: LLMBAs in the Era of Falling Costs
Appendix. Tools for Local LLM Deployment
Ollamallama.cppGPT-Generated Unified Formatllama.cpp Examplellama-serverllamafileQuantizationPerplexity and KL DivergencePrompts DatasetQuantization ExampleResultsLocal Tools, Real Power
Index
About the Authors

Content preview from Large Language Models: The Hard Parts

About the Authors

Dr. Thársis T. P. Souza is a computer scientist and product leader specializing in AI-driven products. He has held leadership roles at some of Wall Street’s largest hedge funds and in early-stage Silicon Valley technology startups. He is the creator of podcastfy.ai and a former lecturer in the Master of Science in Applied Analytics program at Columbia University. He holds a Ph.D. in computer science from University College London, as well as an M.Phil. and M.Sc. in computer science and a B.Sc. in computer engineering.

Jonathan K. Regenstein, Jr., has spent his career working at the intersection of data, machine learning, technology, and asset management. He is a research affiliate at Georgia Tech’s Financial Services Innovation Lab and an advisor to early-stage AI companies. He holds a B.A. from Harvard University and a J.D. from NYU School of Law. He lives in Atlanta, Georgia, with his wife, three daughters, and three dogs.

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Large Language Models: The Hard Parts

by Thársis T. P. Souza, Jonathan K. Regenstein Jr.

About the Authors

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.