Ask Me Anything Prompting
What if you could ask the same question multiple ways and let the answers vote on the truth? AMA Prompting transforms any NLP task into open-ended question-answer format, collects diverse reformulations, and aggregates responses — enabling smaller models to punch far above their weight class.
Introduced: Ask Me Anything (AMA) was published in 2023 by Arora et al. The technique addresses a fundamental limitation of prompt engineering: the way you phrase a question dramatically affects the quality of the answer. Instead of hoping one prompt formulation works well, AMA systematically converts arbitrary NLP tasks into open-ended QA format through multiple reformulations, collects predictions from each, and aggregates answers using weak supervision. The original research demonstrated that this approach enabled 6B-parameter models to match or exceed the performance of 175B-parameter models on certain benchmarks.
Modern LLM Status: Ask Me Anything’s core insight — reformulating tasks into question-answer format and aggregating diverse responses — influenced modern ensemble and self-consistency approaches. While the specific weak supervision pipeline is less common with large models in 2026, the principle of reformulating inputs for better outputs remains a practical prompt engineering technique. The idea that asking the same question in multiple ways yields more reliable answers has been absorbed into best practices for critical decision-making with AI.
Ask It Many Ways, Let the Answers Agree
A single prompt is a single lens — it captures one angle of a problem and may miss critical perspectives. AMA Prompting solves this by converting your task into multiple open-ended questions, each phrased differently. Instead of asking “Is this sentence positive or negative?” once and hoping for the best, AMA generates several reformulations: “How does the author feel?”, “What is the emotional tone?”, “Would someone reading this feel encouraged or discouraged?”
The power is in the aggregation. Each reformulation produces its own prediction. Some will be strong, some weak, some noisy. AMA uses weak supervision techniques to weight and combine these predictions — effectively letting the answers “vote” on the correct response. This statistical approach compensates for the fragility of any single prompt formulation.
Think of it like polling multiple witnesses to an event rather than relying on one account. Each witness has a different vantage point, and by aggregating their testimonies, you get a far more reliable picture of what actually happened.
Simply asking the same question multiple times and taking the majority answer (as in self-consistency) can help, but it biases toward whichever framing you chose. AMA goes further: by fundamentally reformulating the question each time, it accesses different reasoning pathways in the model. A question framed as sentiment analysis may trigger different internal representations than the same task framed as emotional interpretation — and combining both yields answers that neither framing alone could reliably produce.
The AMA Process
Four stages from single task to aggregated prediction
Define the Original Task
Start with any NLP task — classification, extraction, reasoning, or generation. The task may originally be in a format that is not well-suited for open-ended language models, such as a multiple-choice format or a structured label assignment.
Task: Determine whether the following product review is positive, negative, or neutral. Review: “The battery lasts forever, but the screen is dim and the software crashes constantly.”
Generate Multiple QA Reformulations
Convert the task into several different open-ended question-answer pairs. Each reformulation frames the same underlying task from a different angle, using different vocabulary and structure. The goal is diversity — each reformulation should access a different reasoning pathway in the model.
Reformulation 1: “Based on this review, would the customer recommend this product to a friend?”
Reformulation 2: “What overall feeling does this review express about the product?”
Reformulation 3: “If you read this review, would you buy the product?”
Reformulation 4: “Does the reviewer seem satisfied or dissatisfied overall?”
Collect Predictions from Each Reformulation
Run each reformulated prompt through the language model independently and record the response. Some responses will be confident and clear, others may be ambiguous or noisy. This variance is expected and is precisely what the aggregation step leverages.
Response 1: “Probably not — the software issues would be a deal-breaker.” (Negative signal)
Response 2: “Mixed — good battery but poor screen and software.” (Mixed/Negative signal)
Response 3: “I would hesitate due to the crashes.” (Negative signal)
Response 4: “Dissatisfied overall, despite the battery praise.” (Negative signal)
Aggregate Using Weak Supervision
Combine all predictions using a weak supervision approach. Rather than simple majority voting, AMA weighs each reformulation’s reliability and correlations to produce a final, more accurate prediction. The aggregation accounts for the fact that some reformulations are inherently better signal sources than others.
Aggregated result: Negative. Three of four reformulations produced clear negative signals, and the fourth (mixed) still leaned negative. The weak supervision model assigns high confidence to this prediction despite the battery praise in the original review.
See the Difference
Why multiple reformulations outperform a single prompt
Single Prompt
Classify the following text as either “entailment”, “contradiction”, or “neutral”. Premise: “The dog chased the cat up the tree.” Hypothesis: “An animal climbed a tree.”
Neutral. The premise mentions a cat going up a tree, but it does not explicitly say the cat climbed the tree.
AMA Reformulations
Q1: “If the dog chased the cat up the tree, is it true that an animal climbed a tree?” → “Yes, the cat went up the tree.”
Q2: “Does the statement about a cat being chased up a tree support the idea that an animal climbed?” → “Yes, going up a tree involves climbing.”
Q3: “Can we conclude an animal climbed a tree from knowing a cat was chased up one?” → “Yes, the cat is an animal and it went up the tree.”
Entailment. All three reformulations agree: a cat (an animal) going up a tree constitutes climbing, making the hypothesis a valid inference from the premise.
Practice Responsible AI
Always verify AI-generated content before use. AI systems can produce confident but incorrect responses. When using AI professionally, transparent disclosure is both best practice and increasingly a legal requirement.
48 US states now require AI transparency in key areas. Critical thinking remains your strongest tool against misinformation.
AMA in Action
See how reformulating prompts and aggregating answers improves reliability
“Classify this news headline into one of these categories: Politics, Sports, Technology, Health. Headline: ‘Federal Reserve Raises Interest Rates Amid Inflation Concerns’”
Reformulation 1: “What topic does a headline about the Federal Reserve and interest rates belong to?”
Answer: Economics/Politics — monetary policy is a government function.
Reformulation 2: “If someone interested in government policy read this headline, would it be relevant to them?”
Answer: Yes, the Federal Reserve’s rate decisions are a core policy topic.
Reformulation 3: “Is this headline about athletic competition, medical science, digital innovation, or governance?”
Answer: Governance — it concerns a federal institution’s economic decisions.
Aggregated result: Politics. All three reformulations converge on the governance/policy category.
Always verify AI classifications against the original source material before using them in any decision-making process.
“Is the following claim true or false? Claim: ‘The Great Wall of China is visible from space with the naked eye.’”
Reformulation 1: “Can astronauts in orbit see the Great Wall of China without telescopes?”
Answer: No — multiple astronauts have confirmed it is not visible to the naked eye from orbit. The wall is narrow compared to its length.
Reformulation 2: “What human-made structures are visible from space, and is the Great Wall among them?”
Answer: Cities and large infrastructure are visible, but the Great Wall is too narrow (about 15-30 feet wide) to be distinguished from orbit.
Reformulation 3: “Why do people believe the Great Wall is visible from space, and is this belief accurate?”
Answer: This is a popular myth, but it has been debunked by astronauts and space agencies.
Aggregated result: False. All reformulations agree the claim is a common misconception.
AI responses about factual claims should always be verified against authoritative sources. Even with AMA aggregation, models can share widespread misconceptions or outdated information.
“Does the instruction ‘employees must not share confidential information with unauthorized parties’ prohibit discussing project details with a contractor who signed an NDA?”
Reformulation 1: “Is a contractor with a signed NDA considered an ‘authorized party’ under this policy?”
Answer: Generally yes — an NDA is typically the mechanism that authorizes information sharing.
Reformulation 2: “What does ‘unauthorized’ mean in a corporate confidentiality context?”
Answer: Unauthorized typically means individuals who have not been formally cleared through agreements like NDAs, security clearances, or role-based access.
Reformulation 3: “Would sharing project details with an NDA-bound contractor violate a standard confidentiality policy?”
Answer: No, assuming the NDA covers the relevant information and the contractor needs the information for their work.
Aggregated result: No, the instruction does not prohibit sharing with an NDA-bound contractor, as the NDA establishes them as an authorized party. However, sharing should be limited to information relevant to the contractor’s work scope.
Legal and policy interpretations from AI should always be reviewed by qualified professionals. AI cannot substitute for legal counsel on confidentiality matters.
When to Use AMA
Best for tasks where a single prompt formulation may not capture the full picture
Perfect For
Sentiment analysis, topic categorization, and intent detection — where rephrasing the classification question from different angles reveals the correct label more reliably.
When working with resource-constrained models where you cannot simply scale up — AMA’s reformulation-aggregation pipeline extracts more performance from less capable models.
Medical triage, legal analysis, or financial assessments where getting the answer wrong has significant consequences — multiple reformulations reduce single-point-of-failure risk.
When the input text is ambiguous, poorly written, or context-dependent — different question framings probe different interpretations and the aggregation resolves ambiguity.
Skip It When
Questions with unambiguous, factual answers — “What is 2 + 2?” gains nothing from reformulation.
When speed is critical — AMA requires multiple model calls per task, multiplying both latency and token cost.
Writing, brainstorming, or artistic tasks where there is no “correct answer” to converge on — AMA is designed for tasks with deterministic outcomes.
Use Cases
Where AMA delivers the most value
Content Moderation
Reformulate toxicity detection from multiple angles — intent-based, impact-based, and policy-based — to catch subtle violations that any single framing might miss.
Document Classification
Classify legal, medical, or technical documents by asking about their content from multiple perspectives — subject matter, audience, purpose, and regulatory domain.
Diagnostic Triage
Frame symptom assessment from multiple clinical angles — by system, severity, and differential diagnosis — aggregating to reduce the risk of overlooking critical indicators.
Customer Intent Detection
Determine what a customer actually wants by reformulating their request as questions about urgency, subject, desired outcome, and emotional state — then aggregating for routing accuracy.
Security Threat Assessment
Evaluate potential security threats by reformulating threat intelligence from attacker motivation, technical capability, target vulnerability, and historical pattern perspectives.
Survey Response Analysis
Analyze open-ended survey responses by reformulating coding questions from thematic, sentiment, and actionability angles to produce more robust categorizations.
Where AMA Fits
AMA bridges single-prompt approaches and ensemble methods
AMA and self-consistency address different sources of variance. Self-consistency samples multiple reasoning paths from the same prompt; AMA generates predictions from different prompts. Combining both — multiple samples from multiple reformulations — provides a double layer of robustness. This hybrid approach is particularly powerful for tasks where both prompt sensitivity and reasoning variance contribute to errors.
Related Techniques
Explore complementary reformulation and ensemble approaches
Reformulate Your Prompts
Try AMA reformulation on your own tasks or build multi-angle prompts with our tools.