A reflection pass is an agentic design pattern where the AI agent reviews and critiques its own generated output against the source material and initial instructions. By adding a logical step in the code for the agent to "reflect" on its summary—asking itself if it missed any key points from the policy documents—it can identify and correct omissions. This self-correction loop directly addresses the goal of improving response completeness before the final answer is returned to the user.

A. Decreasing the temperature parameter makes the model's output more deterministic and less creative, which does not inherently improve the completeness of a summary.

C. The scenario describes an agent that "generates summaries from retrieved policy documents," which is already the definition of a Retrieval Augmented Generation (RAG) pattern. Therefore, this is not a change.

D. Enabling response streaming affects the delivery method of the response (showing it token-by-token) to improve perceived latency, but it does not alter the final content or its completeness.

1. Microsoft Learn, Prompt engineering techniques, "Chained-thought prompting" section: This official documentation describes breaking down a complex task into intermediate steps. A reflection pass is an advanced form of this, where one step is generating a response and a subsequent step is evaluating and refining it. This is implemented in the application logic.

Source: Microsoft Learn, "Prompt engineering techniques". In the context of improving outputs, chaining prompts to have the model review its work is a documented strategy.

2. Shinn, N., Labash, B., & Gopinath, A. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning. arXiv preprint arXiv:2303.11366. (Cornell University). This foundational academic paper defines the "Reflexion" technique where an agent verbally reflects on its past actions and outputs to produce a better plan or refined response in subsequent attempts.

Source: Section 3, "THE REFLEXION FRAMEWORK," describes the process: "The agent uses the feedback to reflect and generate verbal self-reflections... These reflections are then added to the agent’s working memory... to produce a better action in the next trial."

DOI: https://doi.org/10.48550/arXiv.2303.11366

3. Microsoft Learn, "Build your own copilot with prompt flow": Prompt flow in Azure AI Studio is the tool used to implement such complex logic. It allows developers to chain multiple Large Language Model (LLM) calls and code steps, which is the mechanism for implementing a generation step followed by a reflection/evaluation step.

Source: Microsoft Learn, "Build your own copilot with prompt flow". The documentation illustrates creating multi-step workflows, which is the prerequisite for implementing a reflection pass.