Active Recall Session: Prompt Engineering — Instruction-following vs completion behavior

[github-actions]

Learning Objective

“What do I currently believe, and how could that belief be wrong?”

Bucket: Prompt Engineering
Focus: Instruction-following vs completion behavior
Date: 2025-12-26

(What system, concept, or failure surface am I reconstructing from memory?)

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Active Recall (NO NOTES)

1. Definition + Boundary

Instruction following is the guidance given for handling the input, and completion behavior is whether the output should be incrementally given or given all at once at the completion.

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2. Failure Statement

The system fails when the output does not align with the instructions pattern the model is expected to follow, nor does the model comply with the way that the completion should be given.

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3. Mental Model

(Text diagram or Mermaid showing components, data flow, uncertainty, feedback)

Input -> instructions from prompt -> encode -> gather response -> decode -> completion -> output

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4. Mechanism (Causal Chain)

(Write 3–5 linked causal statements explaining why this works)

1. proper instructions set the boundaries that the model is expected to follow
2. completion behavior is based on the expectations of the user group
3. instructions give context to the model
4. incremental completion can work well with chain-of-thought prompting

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5. Constraints & Trade-offs

• Constraint 1: the amount of information the model is expected to output can impact how precise the instructions are
• Constraint 2: if the completion behavior is to wait until the full response is ready, it is important that the response does not take long, otherwise it can turn into a bad user experience.
• Constraint 3: instructions that are ambiguous can lead to poor performance in being able to complete in a timely and accurate manner

6. Transfer Test

(How would this idea change in another domain or system?)

Could be similar to how telephone operators used to work. Give the operator incomplete information and the operator has bad instructions on how to find the information, the response time could take some time.

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Self-Assessment (Rubric)

Dimension	Score (0–4)	Notes
Conceptual Clarity	3
Mental Model Integrity	3
Causal Understanding	4
Failure Awareness	2
Trade-off Judgment	1
Transfer Ability	3
Calibration & Honesty	3

Initial Confidence (0–100%): 80

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Falsification Plan

(One experiment or eval that could prove me wrong)

A known bad prompt that immediately returns output response that is very accurate

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Carry-Forward Insight

(One sentence for Future Me)

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Delayed Recall (Fill Later)

• What did I forget?
• What was oversimplified?
• What was wrong?

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Completion Checklist

• Explained aloud without notes
• Identified ≥1 real failure mode
• Made a falsifiable claim
• Scored honestly

Confidence Delta Reflection (Fill After Review)

• Initial confidence:
• Reviewer signal (over / under / calibrated):
• My assessment:
• What I will adjust next time:

[coderabbitai]

📝 CodeRabbit Plan Mode

Generate an implementation plan and prompts that you can use with your favorite coding agent.

• Create Plan

Examples

• Example 1
• Example 2

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🔗 Similar Issues

Related Issues

• Daily Recall #86
• 🤖 Agent Recall: #79
• Active Recall Session: Prompt Engineering — Prompt brittleness under schema changes #102

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[pertrai1]

@learning-review-bot review

[github-actions]

Learning Review

1. What Was Done Well

The author demonstrates a clear grasp of the distinction between instruction-following and completion behavior, which aligns with the selected Focus. The causal chain is logically structured and effectively emphasizes the relationship between appropriate instructions and user expectations. Additionally, the inclusion of constraints and trade-offs shows an awareness of the complexities involved in prompt engineering.

2. Where the Thinking Drifted

The response begins to drift from the stated Focus when discussing mental models and transfer tests. While the examples provided are illustrative, they do not directly relate to the core concepts of instruction-following and completion behavior, potentially leading to conceptual misalignment.

3. One Key Misconception

The assertion that "proper instructions set the boundaries that the model is expected to follow" overlooks the possibility that rigid instructions may also limit the model's creative output or adaptability, which is a crucial aspect of many modern AI systems.

4. One Next Thinking Step

The author should explore how variations in instruction specificity might change completion behavior rather than assuming a direct, positive correlation; questioning whether more specific instructions always yield better outputs could yield valuable insights.

5. Calibration Feedback

The author's confidence appears somewhat misaligned with the demonstrated understanding, particularly in the areas of failure awareness and trade-off judgment, where lower scores suggest a less nuanced understanding than claimed.

6. Confidence Delta Prompt

The author appears overconfident, as indicated by the higher initial confidence score relative to lower scores in key dimensions that reflect deeper understanding and awareness of complexities.

7. External Research

Research in the field of natural language processing suggests that the clarity and specificity of prompts significantly impact the performance of AI models. Studies, such as those exploring prompt engineering techniques in transformer models, underline the importance of careful instruction design to optimize model outputs.

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This review evaluates thinking structure and calibration.
It does not assess correctness.

[pertrai1]

Article about following instructions with human feedback - https://arxiv.org/pdf/2203.02155