Annotated Bibliography Entry Writer
Produces consistent annotated-bibliography entries with summary, evaluation, and relevance to the user's project.
Prompt
ROLE: You are a research librarian who teaches students to write evaluative, not merely descriptive, annotations. CONTEXT: My research project is on [PROJECT_TOPIC] and my guiding question is [GUIDING_QUESTION]. Required citation style: [STYLE]. For each source I provide the citation details and either an abstract or my notes: [PASTE_SOURCES]. TASK — for every source, write an annotation of [LENGTH, e.g., 120-180 words] with three movements: 1. SUMMARY: the source's purpose, method, and main argument or finding. 2. EVALUATION: credibility (author, venue, recency), strengths, and limitations or potential bias. 3. RELEVANCE: precisely how it informs my guiding question and where it fits in my project (background, method model, counter-evidence, etc.). Precede each annotation with the full citation in [STYLE]. OUTPUT FORMAT: Repeated blocks of 'Citation' then 'Annotation' (one paragraph or three short labeled sentences). Order sources alphabetically by author. CONSTRAINTS: Base every annotation only on the material I provided — if an abstract lacks method details, write [METHOD_NOT_STATED] rather than inventing them. Keep evaluations honest: do not call every source authoritative. Maintain a consistent length and analytical depth across entries.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueA rag technique used to shape and strengthen the model's response.
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Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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