Product Concepts and Frameworks

94 concepts in Artificial Intelligence, page 3 of 5

• How to Reduce LLM Cost and Latency: Tokens and Caching

  Calculate LLM request costs and evaluate prompt caching, shorter outputs, model routing, streaming and parallel calls without hiding quality trade offs.

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• How to Validate LLM Output with JSON Schema and Business Rules

  Design LLM output schemas, represent missing values and validate syntax, structure, source evidence and business rules before using generated data.

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• How to Write LLM Prompts: Instructions, Examples and Format

  Write clearer LLM prompts with explicit rules, few shot examples and output requirements. Learn how to handle ambiguity and test prompt structure.

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• Human oversight

  What it takes for a person in the loop to be oversight and no mere signature, and the three arrangements worth knowing apart.

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• Interpretability

  What the field has established about the inside of a model, what it has not, and why a system that explains itself is not explaining itself.

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• LLM Application Security: Guardrails, Permissions and Validation

  Secure LLM applications with access controls, output validation, scoped tools and approval checks. Test what happens when model instructions fail.

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• LLM Context Rot: Long-Context Failures and Fixes

  Understand context rot, lost in the middle effects, conflicting evidence and polluted agent memory, with practical tests for long context reliability.

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• LLM Context Windows: Token Limits and Budgeting

  Understand LLM context window limits and build a token budget for instructions, retrieved documents, history, tool results and generated output.

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• LLM Engineering Capstone from Prototype to Release Review

  Complete a practical LLM capstone with prompts, retrieval, validated outputs, controlled tools, evaluation evidence and a rehearsed release decision.

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• LLM Evaluation Datasets: Sampling, Labels and Test-Set Leakage

  Build representative LLM evaluation datasets from real and designed cases, prevent leakage and interpret small sample results with appropriate uncertainty.

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• LLM Function Calling: Tool Design, Validation and Execution

  Learn how LLM function calling works and design tool names, schemas, permissions and results that an application can validate before execution.

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• LLM Grounding Explained: Sources, Citations and Hallucinations

  Learn how LLM grounding connects answers to evidence, how it differs from RAG and how to check citations, source quality and unsupported claims.

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• LLM Model Migration: Evaluation, Compatibility and Rollout

  Plan an LLM model migration with API compatibility checks, controlled evaluations, prompt adaptation, canary rollout and a viable fallback.

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• LLM Observability: Traces, Tool Calls and Replay

  Trace LLM requests across retrieval, model calls, validation and tools. Learn what to record, how to protect data and where replay stops being reproducible.

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• LLM Production Monitoring: Quality, Cost, Latency and Drift

  Monitor LLM applications for answer quality, retrieval failures, tool errors and drift, with meaningful samples, denominators and actionable alerts.

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• LLM Prompt Chaining: Workflow Design, Reliability and Latency

  Learn when to split an LLM task into a prompt chain, how to validate intermediate steps and how dependencies affect reliability, cost and latency.

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• LLM Prompt Patterns: System Prompts and Reasoning Techniques

  Choose effective system prompts, role prompts, decomposition and self consistency techniques, with examples and clear limits for each pattern.

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• LLM Structured Output: JSON Mode, Schemas and Function Calling

  Compare JSON prompting, JSON mode, schema constrained output and function calling. Learn what each guarantees and how to handle incomplete responses.

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• LLM Temperature, Top-p and Deterministic Output Explained

  Understand temperature, top p, top k and seeds in LLM generation, including why temperature zero does not guarantee reproducible responses.

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• LLM-as-a-Judge Evaluation: Rubrics, Bias and Calibration

  Use an LLM judge to score open ended responses with a clear rubric, then measure agreement, false passes and position bias against human labels.

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