Generative AI / Basic: generation and evidence foundations

Generative AI β€” Basic: generation and evidence foundations

Separate models, tokens, prompts, structured form, vector similarity, and factual support before building an application.

6 lessons Β· No account required

What you will learn

  • Explain generation without turning fluency into truth or authority.
  • Specify bounded input and output contracts with honest proof limits.
  • Distinguish representation, evidence, uncertainty, and abstention.

  1. Models, generation, and system boundaries

    Separate learned generation from application policy, factual verification, and permission to act.

    Start learning

  2. Tokens, context, and information budgets

    Distinguish tokenizer-specific length, effective context use, and deliberate evidence selection rather than assuming a word count measures capacity.

    Start learning

  3. Prompts as task contracts

    Specify task, evidence, output, and abstention clearly while treating prompt changes as testable behavior changes rather than magic phrases.

    Start learning

  4. Structured outputs and validation

    Separate parseable syntax, schema conformance, domain validity, factual support, and authority to use a generated result.

    Start learning

  5. Embeddings, similarity, and index identity

    Interpret vector similarity as a model-dependent retrieval signal with explicit dimensions, versioning, authorization, and evaluation limits.

    Start learning

  6. Factuality, uncertainty, and abstention

    Distinguish factual error, unsupported context claims, calibrated confidence, and an honest insufficient-evidence response.

    Start learning
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