• “Draft a ToR outline for a midline evaluation of a [sector] program targeting [population], including scope, methodology options, and a deliverables timeline.”
• “Propose three sampling approaches for surveying [number] households across [number] program sites, with trade-offs for cost, time, and statistical power.”
• “Turn this program logic model into an evaluation matrix with evaluation questions, indicators, data sources, and methods for each objective.”
• “List the ethical considerations I should address in an evaluation involving [vulnerable population].”
• “Draft evaluation questions for a [type] evaluation aligned with the relevance, effectiveness, efficiency, impact, and sustainability criteria.”
• “Compare a quasi-experimental design and a most-significant-change approach for this program, and recommend one given a [budget] and [timeframe] constraint.”
• “Turn these program objectives into SMART indicators with baseline, target, and means of verification.”

• “Draft a data collection checklist for enumerators conducting household surveys on [topic], including common data-quality pitfalls to avoid.”
• “Write 10 survey questions to measure [outcome] using a Likert scale, avoiding leading or double-barreled phrasing.”
• “Review this survey instrument and flag any questions that could introduce bias or confusion for respondents with limited literacy.”
• “Draft a data quality assurance protocol for field supervisors reviewing enumerator-collected data on a weekly basis.”
• “Suggest a plan for triangulating qualitative interview data with quantitative survey results on [topic].”
• “Write informed consent language for a household survey involving minors, in plain, accessible language.”
• “Draft a data cleaning checklist for a dataset with [number] variables collected via mobile data collection.”

• “Summarize the key patterns in this indicator dataset and suggest two hypotheses worth investigating further.” (attach your data)
• “Draft an outline for a mixed-methods analysis combining survey results and focus group transcripts on [topic].”
• “Identify contradictions between these two data sources and suggest how to reconcile them in a findings section.”
• “Turn this list of qualitative themes into a coded framework with a definition for each code.”
• “Write a plain-language explanation of statistical significance for a non-technical program team reading these results.”
• “Draft three alternative interpretations of this finding, along with the evidence that would support or rule out each one.”
• “Synthesize these five case studies into common cross-cutting lessons and one contextual difference worth noting.”

• “Draft an executive summary for this evaluation report that leads with the three most important findings.”
• “Rewrite this technical findings section in plain language for a donor audience with no MEAL background.”
• “Draft recommendations that are specific, actionable, and assigned to a plausible responsible party, based on these findings.”
• “Turn this 40-page evaluation report into a two-page learning brief.”
• “Draft a one-slide summary of this evaluation’s methodology, key findings, and top three recommendations.”
• “Suggest a title and subtitle for this report that accurately reflects the findings without overstating them.”
• “Write a short abstract of this evaluation suitable for publication on our website.”

• “Design a MEL plan structure for a program with these outcomes, including indicator, frequency, and responsible team member.”
• “Draft a monthly indicator-tracking reminder message to send to field teams before each reporting deadline.”
• “Review this indicator list and flag any that are difficult to measure reliably or lack a clear data source.”
• “Propose a dashboard layout for tracking these five indicators over a two-year program period.”
• “Draft guidance notes explaining how to correctly calculate this indicator, for a field team unfamiliar with the definition.”
• “Suggest a QA process for catching indicator reporting errors before they reach a donor report.”
• “Write a short explainer distinguishing outputs, outcomes, and impact for a team new to MEAL.”

• “Draft an email to a program team explaining a difficult evaluation finding constructively, without becoming defensive or dismissive.”
• “Write talking points for presenting these findings to a skeptical donor audience.”
• “Draft a response to a peer reviewer’s critical comments on this evaluation’s methodology section.”
• “Turn these findings into three key messages for a stakeholder validation workshop.”
• “Draft an agenda for a findings-validation workshop with program staff and community representatives.”
• “Write a short FAQ anticipating the questions a donor is likely to ask about this evaluation’s limitations.”
• “Draft a respectful way to communicate a program’s underperformance against target to field staff.”

• “Draft three concrete adaptations a program could make based on this evaluation’s findings.”
• “Write a short after-action review template for a program team to use after a difficult project phase.”
• “Summarize what this evaluation suggests should be carried into the design of a future phase.”
• “Draft a set of reflection questions for a program team’s quarterly learning session.”
• “Identify which of these recommendations are low-cost and high-feasibility versus which require significant redesign.”
• “Draft a one-page lessons-learned summary to feed into an organizational knowledge base.”
• “Suggest how this evaluation’s findings could inform a related program in a different country context.”

• “Summarize this week’s field reports into a five-bullet update for my supervisor.”
• “Draft a polite follow-up email to a partner organization that hasn’t submitted requested data.”
• “Turn my meeting notes into a clear list of action items with owners and deadlines.”
• “Draft an out-of-office message for a two-week field visit with limited connectivity.”
• “Proofread this paragraph for clarity and tone before I send it to a donor.”
• “Summarize this long email thread into three sentences I can act on quickly.”
• “Draft an agenda for a 30-minute team check-in on evaluation progress.”

The wording works across all three. What differs is how well each tool grounds the answer in an attached document — always check whether your tool actually read the file you attached rather than answering from general knowledge.

Check your organization’s data policy first. Personally identifiable information about beneficiaries generally shouldn’t be pasted into a consumer AI tool unless your organization has confirmed the tool’s data handling meets its standards.