Agent Application DevelopmentAccount
Knowledge catalogChoose core direction and segmented content
Practice 62FundamentalsConceptsAbout 16 minutes

Corresponding knowledge: Boundaries between learned capabilities, contextual evidence, and task behavior

What do pretraining, RAG and fine-tuning change? Why are corporate knowledge questions and answers often used in combination?

Distinguish between capabilities in parameters, evidence in requests, and post-training behavior, and select adaptation methods based on failure reasons.

Learn corresponding knowledgeInterview questions · Answer independently →
pre-trainingRAGfine-tuningModel adaptation

Knowledge content check2026-10-03 · Check the source of the original question2026-10-03

The knowledge corresponding to this exercise:

Prompts, structured output, and iterative acceptance checks · Hierarchical deep learning →
Knowledge unit directory

LEARN · PRACTICE · REFLECT

Knowledge exercises·Independent answers

My notes and review ↗

Principles and Solutions have been collapsed. Explain the core mechanism, boundaries and verification methods in your own words, and then compare them.

Answers and personal notes

Each modified commit will be kept as an independent history. Your level of mastery is up to you to evaluate yourself against the standards.

Explain in your own words first

The core principles, analysis, Q&A and migration cases have been closed. When you are ready, unfold it and compare it with the content to find any omissions.

Hands-on verificationComplete on demand · Suggestions15 minutes

Attribute the four types of failures "the policy has been updated but the model answers the old value", "the evidence is complete but fields are missing", "the current user revokes rights" and "stable classification without documentation", respectively, and design minimum comparisons.

Expand acceptance requirements and checkpoints
  • The method chosen directly addresses the lack of information or behavioral failure and does not treat the three techniques as equivalent options.
  • Make it clear that fact updates and permissions are obtained from trusted external states, and training rejections do not replace authorization.
  • Test new values, no evidence and unseen wording, fix evidence comparison behavior and check end-to-end results

Key inspections

  • Can distinguish between changing parameters during training and providing context during inference, and understand that the two can be combined
  • We don’t say that fine-tuning can only be about learning styles, nor do we say that RAG means automatically ensuring authenticity.
  • Ability to select methods based on missing information or failed behavior, and place the current authorization on the trusted server
  • Evaluate combination gains with independent testing and condition changes to avoid leakage of training answers