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AnthropicAI prompt engineering: A deep dive
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At a glance
WHAT IT’S REALLY ABOUT
Prompt engineering is iterative, context-rich communication that treats prompts like code
- The panel defines prompt engineering as the practice of eliciting the best possible model behavior through clear communication, rapid iteration, and system-aware integration rather than a single “perfect” prompt.
- They describe strong prompt engineering as the ability to state tasks without hidden assumptions, anticipate edge cases and adversarial or messy inputs, and validate by closely reading outputs.
- The speakers debate personas, honesty, and metaphors in prompts, with a strong preference for directly describing the real task and context rather than roleplay shortcuts that omit key nuance.
- They discuss model reasoning and chain-of-thought as a technique that tends to improve performance in practice, while cautioning against over-personifying what “reasoning” means internally.
- They distinguish consumer/research/enterprise prompting goals and predict a future where models increasingly help write prompts by interviewing users, generating examples, and identifying ambiguities.
IDEAS WORTH REMEMBERING
5 ideasPrompt engineering is iterative experimentation, not just writing.
He framed the “engineering” part as systematic experimentation: you can restart from scratch, try variations independently, and iteratively converge on better results. Several speakers reinforced that iteration is central, not an afterthought.
In production, prompting is systems design as much as instruction writing.
He argued that prompts don’t live in isolation: you must consider where data comes from, what context is available, and trade-offs like latency and how much data to include. He also noted teams increasingly treat prompts like code, including tracking experiments and versions.
Great prompts are defined by edge-case thinking and graceful failure modes.
She emphasized that good prompting includes anticipating atypical inputs (e.g., empty inputs, missing target entities, malformed user text) and specifying behavior for edge cases. She also recommended giving the model an explicit “out” (e.g., output an “unsure” tag) rather than forcing a guess.
A core skill is externalizing assumptions and using the model to find gaps.
Multiple speakers said people often under-specify tasks because they forget what the model doesn’t know, and models don’t reliably ask clarifying questions the way a human coworker would. She described asking the model to critique the prompt for ambiguities and even propose edits after a failure.
Eliciting and structuring model reasoning is useful, even if imperfectly faithful.
They discussed that structured reasoning tends to improve outcomes, even if the “reasoning” text isn’t always perfectly faithful to the internal process. A speaker focused on practical results: reasoning “just works,” and structuring it can help even more.
WORDS WORTH SAVING
5 quotesI think the engineering part comes from the, the trial and error.
— Zack Witten
Uh, but it kind of is true that now we write essays and treat them like code, and I think that's actually correct.
— David Hershey
I think I don't trust the model ever, and then I just hammer on it.
— Amanda Askell
I think also the very best of prompting can kind of make the difference between a failed and a successful experiment.
— Amanda Askell
Like, it's really interesting that there's at least that similarity of just, like, taking things that are in your brain, analyzing them enough to feel like you fully understand them, and could take any person off the street who's, like, a reasonable person and just, like, externalize your brain into them, and I feel like that's, like, the core of prompting.
— Amanda Askell
SPEAKERS
Alex Albert
hostLeads Developer Relations at Anthropic and moderates discussions on applied LLM usage and prompt engineering.
David Hershey
guestWorks with customers at Anthropic on deploying LLMs, including prompting, system-building, fine-tuning, and evaluation practices.
Amanda Askell
guestLeads a fine-tuning team at Anthropic focused on training and aligning Claude (e.g., honesty and helpfulness).
Zack Witten
guestPrompt engineer at Anthropic focused on designing, testing, and integrating prompts into real systems.
High quality AI-generated summary created from speaker-labeled transcript.
