Unverified50% confidenceOpinionExact time
Intent-based prompting and in-context learning complement each other: the model infers the task pattern from the examples and background provided in the prompt, while the intent description provides a higher-level semantic anchor for this inference.
1
Sources
50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedProviding in-context learning examples at inference time can substantially mitigate context confusion.79% similarUnverifiedIntent-based prompting emphasizes explaining "why this output is needed," prompting the model to reason about the goal during generation, thereby aligning more closely with real scenarios in information filtering, depth adjustment, and format selection.78% similarUnverifiedExperience distillation and context injection are the implementation foundation of the framework-level recursion; framework knowledge is stored in natural language form, does not change model parameters, and is instead dynamically concatenated into the prompts or context of subsequent tasks.78% similarVerifiedThe effectiveness of prompt engineering is rooted in the large language model's In-Context Learning capability, which enables task switching without modifying model weights.76% similarUnverifiedContextual framing conveys implicit expectation signals to the model through scenario descriptions, role assignments, or phrasing cues, without the need to issue direct instructions.76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1101050API
curl https://kongchang.com/api/v1/knowledge/claims/1101050MCP
get_claim(id=1101050)