Claude Certified Associate - Foundations flashcards

32 cards. Read the term and the definition here. The deck at the top of this page is optional.

Haiku model tier

Claude's smallest, fastest model family, optimized for low latency and low cost per token; suited to high-volume classification, routing, and simple extraction tasks.

Sonnet model tier

Claude's mid-tier family balancing capability, speed, and cost; typically the default choice for production workloads needing strong reasoning without Opus-level expense.

Opus model tier

Claude's most capable and most expensive family, used for complex multi-step reasoning, difficult coding, and analysis where answer quality outweighs added latency and cost.

Dated model ID

Anthropic model identifiers combine family, version, and release date, such as claude-3-5-sonnet-20241022; pinning the full dated ID keeps production behavior reproducible over time.

Constitutional AI training

Anthropic's training method in which a model critiques and revises its own outputs against a written set of principles, reducing reliance on human labels for harmful content.

Extended thinking

An optional capability on some Claude models that produces internal reasoning before the final answer, improving hard tasks while consuming additional output tokens and latency.

System prompt function

A top-level instruction block setting persona, rules, and output constraints before the conversation turns; it steers behavior more strongly than content inside user messages.

XML tag structuring

Wrapping prompt sections in tags such as instructions, document, or example helps Claude parse boundaries, especially when long reference text and directives are mixed together.

Few-shot prompting

Including several worked input-output examples in the prompt so the model infers the target format and decision pattern; three to five diverse examples usually suffice.

Assistant turn prefill

Placing partial text in the first assistant turn forces the model to continue from that point, useful for enforcing JSON, XML, or single-label output formats.

Chain-of-thought prompting

Asking the model to reason step by step before giving a final answer, which raises accuracy on math, logic, and multi-constraint tasks at the cost of more tokens.

Injection in retrieved text

Untrusted text placed in context may contain fake instructions; delimit it clearly and instruct the model to treat that content as data rather than as commands.

Messages API endpoint

Claude is called by HTTP POST to /v1/messages with headers x-api-key and anthropic-version; required body fields are model, max_tokens, and messages.

Message role alternation

The messages array must start with a user turn and alternate between user and assistant; system-level rules belong in the separate top-level system parameter.

stop_reason values

Response field showing why generation ended: end_turn for natural completion, max_tokens for truncation, stop_sequence for a matched string, and tool_use for a tool call.

Tool use flow

You declare tools with name, description, and JSON input_schema; the model returns a tool_use block, and you send results back in a user tool_result block.

Rate limit handling

Responses report remaining requests and tokens per minute; on a 429 or 529 error, retry with exponential backoff and jitter instead of resending immediately.

Streaming SSE events

Setting stream to true returns server-sent events including message_start, content_block_delta with incremental text, and message_stop; this cuts perceived latency, not token cost.

Token to word estimate

For English text roughly one token equals four characters, or about 0.75 words; use the count_tokens endpoint for exact figures before sending large payloads.

max_tokens truncation

max_tokens caps generated output only; if generation reaches the cap, stop_reason is max_tokens and the reply ends mid-thought, so size it for the longest expected answer.

Prompt caching breakpoints

Marking stable prompt prefixes with cache_control lets repeated requests reuse processed tokens, cutting cost and latency; static content belongs first and variable content last.

Context window budget

The context window limits combined input plus output tokens for a single request; exceeding it fails the call, so trim, summarize, or retrieve before sending.

Image token cost

Images are converted into tokens roughly proportional to pixel area, so a large image can cost more than a page of text; downscale before sending when fine detail is unnecessary.

Helpful honest harmless

Anthropic's three training objectives: assist the user, avoid asserting falsehoods and express uncertainty instead, and refuse to cause harm; these goals can conflict and are balanced during training.

Prompt injection defense

Treat all retrieved or user-supplied content as untrusted data: delimit it, never let it redefine system rules, and require confirmation before acting on tool calls it suggests.

Human in the loop

Placing a person as approver for consequential actions such as payments, deletions, medical or legal guidance, and any irreversible tool call the model proposes.

Sycophancy risk

Models may agree with an incorrect user premise; counter this by asking for an independent assessment before revealing your own view or the answer you expect.

PII redaction practice

Removing names, identifiers, and secrets from text before sending it to any API; use placeholders and map them back locally to limit exposure and support compliance obligations.

LLM as judge

Using a model to grade outputs against a written rubric; supply the rubric, require reasoning before the score, and validate judge agreement against human labels.

Regression eval suite

A fixed set of inputs with expected outputs or rubrics, rerun whenever the prompt, model version, or parameters change so quality drops surface before release.

Model version pinning

Specifying the fully dated model ID in production so silent upgrades cannot change behavior; test new versions explicitly before shifting traffic to them.

Error analysis loop

Sampling real failures, grouping them into categories, and ranking by frequency times impact to decide which prompt or pipeline fix to attempt next.