GUIDES
60 reusable mental models for AI.
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Understand AI
19 Guides
Models, Tokens & Generation·FOUNDATION·6 min·Practice availableProbabilistic model behaviorTreat model output as a distribution of plausible continuations, not a deterministic lookup from question to answer.Used in 2 courses · AI Foundations · LLM Application EngineeringRead Guide Models, Tokens & Generation·FOUNDATION·6 min·Practice availableModel claim vs runtime factA model can describe what it thinks happened; only the runtime and external systems can establish what actually happened.Used in 2 courses · AI Foundations · Data Analysis with AIRead Guide Models, Tokens & Generation·FOUNDATION·5 min·Practice availableTokenizationModels process token sequences, not human-visible characters or words, so cost, limits and behavior depend on how text is segmented.Used in 1 courses · AI FoundationsRead Guide Models, Tokens & Generation·FOUNDATION·6 min·Practice availableContext windowThe context window is a finite working-input budget shared by instructions, conversation, retrieved evidence and tool results.Used in 1 courses · AI FoundationsRead Guide Context & Representation·FOUNDATION·5 min·Practice availableRelevance over raw volumeMore input can reduce decision quality when low-value material competes with the evidence that actually matters.Used in 2 courses · AI Foundations · RAG & Knowledge SystemsRead Guide Inference & Model Selection·INTERMEDIATE·6 min·Practice availableModel selection is a workload trade-offChoose models against the workload and its constraints rather than assuming the largest or newest model is automatically the best choice.Used in 3 courses · AI Foundations · LLM Application EngineeringRead Guide AI Safety & Governance·FOUNDATION·6 min·Practice availableCopyright, licensing and provenanceAI-assisted creation needs provenance records that distinguish what came from sources, what was transformed and what may be reused or published.Used in 3 courses · AI Foundations · Create a Course or Knowledge Product with AIRead Guide Training & Post-training·ADVANCED·7 min·Practice availableSynthetic data and distillationSynthetic data can scale examples and transfer behavior, but it also transfers the generator's blind spots unless quality and diversity are controlled.Used in 2 courses · AI Foundations · Model EngineeringRead Guide Training & Post-training·INTERMEDIATE·7 min·Practice availableSupervised fine-tuningSupervised fine-tuning adapts model behavior using curated input–target examples, but it should solve a demonstrated behavior gap rather than replace application design.Used in 2 courses · AI Foundations · Model EngineeringRead Guide Embeddings & Multimodal·FOUNDATION·6 min·Practice availableVector similarity is a retrieval signalVector similarity estimates closeness in an embedding space; it is useful for candidate retrieval but should not be confused with truth, authority or task relevance.Used in 2 courses · AI Foundations · RAG & Knowledge SystemsRead Guide Reasoning & Planning·INTERMEDIATE·6 min·Practice availableVerification over introspectionWhen correctness matters, check the world or an independent artifact instead of asking the model whether its own reasoning was correct.Used in 3 courses · AI Foundations · Data Analysis with AIRead Guide Limits, Uncertainty & Failure·INTERMEDIATE·6 min·Practice availableUncertainty calibrationUseful uncertainty means confidence tracks observed error risk closely enough to change system behavior, escalation and communication.Used in 2 courses · AI Foundations · Data Analysis with AIRead Guide Training & Post-training·FOUNDATION·7 min·Practice availablePretraining objective and learned behaviorPretraining teaches a model to predict patterns in large datasets; useful capabilities emerge from that objective, but product guarantees do not.Used in 2 courses · AI Foundations · Model EngineeringRead Guide Models, Tokens & Generation·FOUNDATION·6 min·Practice availableModel capability envelopeA model has a workload-dependent envelope of tasks it can perform acceptably; outside that tested envelope, confidence should fall before ambition rises.Used in 2 courses · AI Foundations · LLM Application EngineeringRead Guide Models, Tokens & Generation·FOUNDATION·7 min·Practice availableLatency and throughput basicsAI serving performance is a queueing and workload problem: response time, concurrency and token generation compete for finite compute capacity.Used in 2 courses · AI Foundations · LLM Application EngineeringRead Guide Training & Post-training·INTERMEDIATE·7 min·Practice availablePreference post-trainingPreference post-training shapes which behaviors a model tends to choose among plausible continuations, but it does not turn preference signals into hard application guarantees.Used in 2 courses · AI Foundations · Model EngineeringRead Guide Instructions & Prompting·FOUNDATION·6 min·Practice availablePrompt decompositionBreak a complex request into explicit reasoning or production stages when one prompt mixes different responsibilities, evidence needs or verification methods.Used in 2 courses · AI Foundations · Research with AIRead Guide AI Safety & Governance·FOUNDATION·7 min·Practice availablePrivacy and data boundariesAI systems need explicit rules for what data may enter models, logs, retrieval indexes, memory and third-party tools, because each boundary creates a different exposure and retention risk.Used in 2 courses · AI Foundations · Build an AI Knowledge BaseRead Guide Inference & Model Selection·INTERMEDIATE·7 min·Practice availableOpen and closed model trade-offsOpen-weight and hosted closed models offer different control, operational burden, data boundaries and capability economics; the right choice depends on the system constraint.Used in 2 courses · AI Foundations · Model EngineeringRead Guide
Build AI
31 Guides
LLM Application Engineering·FOUNDATION·6 min·Practice availableStructured output is an application contractA JSON shape is useful only when the application validates both syntax and the meaning required by downstream code.Used in 1 courses · LLM Application EngineeringRead Guide Context Engineering·FOUNDATION·6 min·Practice availableFinite context budgetContext engineering is allocation: decide which instructions, evidence, memory and tool state deserve the next token of budget.Used in 1 courses · LLM Application EngineeringRead Guide Context Engineering·INTERMEDIATE·7 min·Practice availableContext managementContext management continuously decides what to load, refresh, compact and remove as a task evolves.Used in 4 courses · LLM Application Engineering · Research with AIRead Guide RAG & Retrieval·INTERMEDIATE·6 min·Practice availableDense, sparse and hybrid retrievalDifferent retrieval signals catch different evidence; hybrid retrieval combines semantic similarity with exact lexical evidence.Used in 1 courses · RAG & Knowledge SystemsRead Guide RAG & Retrieval·INTERMEDIATE·6 min·Practice availableFreshness, authority and source priorityThe most similar passage is not necessarily the source the system should trust.Used in 5 courses · AI Customer Support · Build an AI Knowledge BaseRead Guide Memory Systems·INTERMEDIATE·6 min·Practice availableWorking memory vs durable memoryKeep temporary reasoning context separate from durable state that must survive compaction, retries and new sessions.Used in 1 courses · RAG & Knowledge SystemsRead Guide Agent Runtime·INTERMEDIATE·6 min·Practice availableAct → observe → verify loopAn agent is a control loop that chooses an action, observes the environment and verifies whether the task state actually improved.Used in 3 courses · Agent Engineering · AI Customer SupportRead Guide Agent Runtime·INTERMEDIATE·6 min·Practice availableBounded autonomyGive an agent enough freedom to complete useful work, but place hard boundaries around cost, time, permissions and irreversible effects.Used in 2 courses · Agent Engineering · AI Customer SupportRead Guide Agent Runtime·INTERMEDIATE·7 min·Practice availableWorkflow decompositionBreak a complex goal into stages with explicit inputs, outputs and verification before adding more agent intelligence.Used in 4 courses · Agent Engineering · Multi-Agent & OrchestrationRead Guide Evaluation & Reliability·INTERMEDIATE·6 min·Practice availableTimeout ambiguityA timeout proves that a timely response was not observed; it does not prove that the remote operation failed.Used in 1 courses · Production AI ReliabilityRead Guide Evaluation & Reliability·INTERMEDIATE·6 min·Practice availableIdempotency boundaryRepeated attempts should represent the same business intent instead of silently creating a new side effect each time.Used in 1 courses · Production AI ReliabilityRead Guide Observability·INTERMEDIATE·6 min·Practice availableObservability as a diagnosis interfaceLogs and traces are valuable when they let an operator reconstruct why an AI outcome happened across model, retrieval, tool and runtime layers.Used in 1 courses · Production AI ReliabilityRead Guide Evaluation & Reliability·INTERMEDIATE·7 min·Practice availableEvaluation environments and verifier designAn AI evaluation is only as credible as the environment, task contract and verifier that determine what counts as success.Used in 6 courses · Agent Engineering · Build an AI Knowledge BaseRead Guide Tools & Function Calling·INTERMEDIATE·6 min·Practice availableHuman review boundariesHuman review is useful when it owns a concrete decision at a consequential boundary, not when a person merely glances at AI output.Used in 5 courses · Agent Engineering · AI Customer SupportRead Guide Vibe Coding & Agentic Coding·FOUNDATION·6 min·Practice availableSpecify before you generateAI-assisted implementation becomes controllable when the intended behavior and acceptance evidence are defined before code is generated.Used in 2 courses · Create a Course or Knowledge Product with AI · Vibe Coding & Agentic Software EngineeringRead Guide Evaluation & Reliability·INTERMEDIATE·6 min·Practice availableConfidence, variance and sample-size humilityAI evaluation results are samples from variable systems, so confidence requires repeated observations, uncertainty estimates and enough data for the decision.Used in 2 courses · Data Analysis with AI · Production AI ReliabilityRead Guide Tools & Function Calling·INTERMEDIATE·6 min·Practice availableValidate tool results before using themA successful tool call only means a response arrived; the runtime still has to validate its shape, semantics and relationship to the intended action.Used in 2 courses · Agent Engineering · AI Customer SupportRead Guide Production Architecture·INTERMEDIATE·6 min·Practice availableVersioned dependencies and change boundariesAI behavior depends on models, prompts, tools, schemas and retrieval sources that change independently, so production systems need explicit version boundaries.Used in 2 courses · Production AI Reliability · Vibe Coding & Agentic Software EngineeringRead Guide Security & Human Control·INTERMEDIATE·6 min·Practice availableAuditability as durable decision evidenceAn auditable AI system preserves enough durable evidence to reconstruct what happened, which rules applied and who or what authorized the consequential action.Used in 2 courses · AI Customer Support · Production AI ReliabilityRead Guide RAG & Retrieval·INTERMEDIATE·7 min·Practice availableRetrieval is a pipeline, not one search callReliable RAG separates candidate generation, filtering, reranking, context assembly and provenance so each failure can be measured and repaired.Used in 3 courses · Build an AI Knowledge Base · RAG & Knowledge SystemsRead Guide Tools & Function Calling·INTERMEDIATE·7 min·Practice availableTool contracts for agentsA tool contract defines what an agent may request, what the runtime guarantees, what can fail and which side effects require stronger controls.Used in 2 courses · Agent Engineering · AI Customer SupportRead Guide RAG & Retrieval·INTERMEDIATE·6 min·Practice availableEvaluate retrieval before generationRetrieval evaluation asks whether the system surfaced the necessary evidence, at usable rank and under the right authority constraints, before judging the generated answer.Used in 2 courses · Build an AI Knowledge Base · RAG & Knowledge SystemsRead Guide Vibe Coding & Agentic Coding·ADVANCED·6 min·Practice availableParallel agent workParallelism helps when subproblems are genuinely independent or provide independent verification; otherwise coordination cost can exceed the speedup.Used in 2 courses · Multi-Agent & Orchestration · Vibe Coding & Agentic Software EngineeringRead Guide Tools & Function Calling·INTERMEDIATE·6 min·Practice availableLeast privilege for AI capabilitiesGive an AI system only the permissions required for the current task, for the shortest practical scope, with stronger gates around irreversible effects.Used in 2 courses · Agent Engineering · AI Customer SupportRead Guide Agent Runtime·INTERMEDIATE·6 min·Practice availableVerification loopsReliable agents repeatedly compare intended state with observed state, then use the gap to decide whether to continue, repair, escalate or stop.Used in 1 courses · Agent EngineeringRead Guide Security & Human Control·INTERMEDIATE·6 min·Practice availableTrust boundaries in AI systemsA trust boundary marks where data, instructions or actions cross between components with different authority, ownership or reliability assumptions.Used in 1 courses · Production AI ReliabilityRead Guide Evaluation & Reliability·INTERMEDIATE·6 min·Practice availableTraceability from input to outcomeTraceability connects the user's request, selected evidence, model decisions, tool actions and final outcome so failures can be reconstructed across layers.Used in 1 courses · Production AI ReliabilityRead Guide Evaluation & Reliability·INTERMEDIATE·7 min·Practice availableSlices and regression suitesAggregate quality is not enough: release evaluation needs stable slices that represent important users, tasks and failure modes, plus regression tests that prevent solved problems from returning.Used in 1 courses · Production AI ReliabilityRead Guide Memory Systems·INTERMEDIATE·7 min·Practice availableMemory lifecycleDurable AI memory needs admission, provenance, update, retrieval and deletion rules; persistence without a lifecycle only makes stale mistakes harder to remove.Used in 2 courses · Build an AI Knowledge Base · RAG & Knowledge SystemsRead Guide Multi-Agent & Orchestration·INTERMEDIATE·7 min·Practice availableOrchestration topology trade-offsLoops, graphs, supervisors and parallel workers solve different coordination problems; topology should be chosen from dependency structure rather than architectural fashion.Used in 1 courses · Multi-Agent & OrchestrationRead Guide Memory Systems·INTERMEDIATE·6 min·Practice availableMemory expiry and invalidationDurable memory should stop influencing decisions when its evidence becomes stale, its scope ends or stronger information supersedes it.Used in 2 courses · Build an AI Knowledge Base · RAG & Knowledge SystemsRead Guide
Use AI
10 Guides
Research with AI·INTERMEDIATE·6 min·Practice availableTriangulate independent sourcesConfidence should increase when independent evidence paths converge, not when many links repeat the same upstream claim.Used in 2 courses · Research with AI · Write a Book with AIRead Guide Long-Form Creation·INTERMEDIATE·7 min·Practice availableLong-form consistencyCoherent long documents externalize claims, terminology, voice and decisions instead of relying on one giant prompt to remember everything.Used in 1 courses · Write a Book with AIRead Guide Work Automation·FOUNDATION·6 min·Practice availableChoose the automation boundaryAutomate stable, observable work and keep humans where ambiguity, accountability or exception cost remains high.Used in 1 courses · Run a Solo Business with AIRead Guide Documents & Data Analysis·FOUNDATION·6 min·Practice availableDocument extraction as evidence-preserving transformationDocument extraction should convert messy source material into structured evidence without erasing location, provenance or uncertainty.Used in 2 courses · Build an AI Knowledge Base · Data Analysis with AIRead Guide Long-Form Creation·INTERMEDIATE·6 min·Practice availableEditorial voice controlConsistent voice comes from explicit editorial constraints and review artifacts, not from repeatedly asking a model to 'sound like us'.Used in 2 courses · Create a Course or Knowledge Product with AI · Write a Book with AIRead Guide AI Knowledge Base·INTERMEDIATE·7 min·Practice availableKnowledge base lifecycleA useful AI knowledge base is a maintained evidence system with ingestion, authority, update, retirement and evaluation policies.Used in 3 courses · AI Customer Support · Build an AI Knowledge BaseRead Guide Media Creation·FOUNDATION·6 min·Practice availableVisual briefing for generated mediaA strong visual brief turns an aesthetic intention into constraints that can be generated, reviewed and revised consistently.Used in 1 courses · Create a Course or Knowledge Product with AIRead Guide Research with AI·FOUNDATION·6 min·Practice availableResearch question decompositionA strong research workflow converts a broad question into smaller claims and evidence needs before searching, so synthesis is driven by what must be established rather than by whichever sources appear first.Used in 2 courses · Research with AI · Run a Solo Business with AIRead Guide Media Creation·INTERMEDIATE·6 min·Practice availableMultimodal iterationText, image, audio and other modalities need iterative review against cross-modal meaning and production constraints, not independent generation followed by assembly.Used in 1 courses · Create a Course or Knowledge Product with AIRead Guide Support & Operations·INTERMEDIATE·7 min·Practice availableCustomer support copilotA support copilot should increase operator leverage by retrieving evidence, structuring cases and drafting actions while keeping source authority and consequential decisions explicit.Used in 2 courses · AI Customer Support · Run a Solo Business with AIRead Guide