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Prompt Engineering Guide

Mastering Meeting notes extraction
on Cerebras Llama 3.1 70B

Stop guessing. See how professional prompt engineering transforms Cerebras Llama 3.1 70B's output for specific technical tasks.

The "Vibe" Prompt

"Extract the key decisions, action items, and attendees from the following meeting transcript. Summarize the main topics discussed. Transcript: [MEETING_TRANSCRIPT]"
Low specificity, inconsistent output

Optimized Version

STABLE
You are an expert at extracting structured information from meeting transcripts. Your goal is to identify key decisions, action items, and attendees, and summarize the main topics. Follow these steps meticulously: 1. **Identify Attendees**: List all individuals mentioned or clearly implied as present in the meeting. Deduplicate names. If a role is mentioned (e.g., 'Project Manager'), include it next to the name if identifiable, otherwise list just the name. 2. **Extract Key Decisions**: Go through the transcript and pinpoint explicit decisions made during the meeting. For each decision, capture: * `decision_text`: The full text of the decision. * `decision_maker`: The person or group making the decision, if identifiable. * `decision_impact`: A brief statement of what the decision implies or affects. 3. **Extract Action Items**: Identify all tasks assigned during the meeting. For each action item, capture: * `action_item_text`: The specific task to be done. * `assigned_to`: The individual(s) responsible for the action. * `due_date`: If a deadline is explicitly mentioned, include it. If not, state 'TBD'. * `status`: Initial status should be 'Open'. 4. **Summarize Main Topics**: Read through the entire transcript and provide a concise summary (1-3 sentences) of the primary subjects discussed, ensuring key themes are covered. Structure your output as a JSON object with the following keys: `attendees` (array of strings), `decisions` (array of objects), `action_items` (array of objects), `summary` (string). Meeting Transcript: [MEETING_TRANSCRIPT]
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverageschain-of-thought by breaking down the task into sequential, explicit steps. It uses clear headings and formatting to guide the model. It also explicitly defines the desired output format (JSON with specific keys and nested structures), reducing ambiguity and improving parsing accuracy. This structured approach helps the model focus on one sub-task at a time, leading to more accurate and complete extractions compared to the vague 'vibe_prompt'. It also reduces hallucination by providing specific instructions on what to look for (e.g., 'explicit decisions', 'explicitly mentioned due date').

5%
Token Efficiency Gain
The output JSON must contain 'attendees' as an array of strings.
Each item in 'decisions' array must have 'decision_text', 'decision_maker', and 'decision_impact' keys.
Each item in 'action_items' array must have 'action_item_text', 'assigned_to', 'due_date', and 'status' keys.

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