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AI can be prompted to generate reasoning examples, assemble a problem-solving structure, compare multiple candidate answers, or critique and revise a draft. These are different methods—not one magic “self-reasoning” switch—and none makes a model’s answer automatically reliable. A separate line of work trains models to use reasoning strategies rather than relying on a user to add “think step by step.”
What it means for AI to prompt itself
Chain-of-thought (CoT) prompting asks a language model to produce intermediate steps alongside an answer. Those steps can encourage decomposition of a multi-part task, rather than jumping straight to a conclusion. Google Research’s overview describes this use of CoT and reports a follow-up self-consistency result of 74% accuracy on GSM8K, a mathematical reasoning benchmark; that figure belongs to the reported benchmark setup, not to language models generally (Google Research).
“Prompt itself” can refer to several ways of automating work that a person might otherwise do when designing a prompt: have the model draft demonstrations, choose a reusable structure, generate several candidate solutions, or give feedback on a draft and revise it. Some approaches instead change how a model is trained. The common theme is using model output to shape later model behavior; the mechanisms and reliability differ.
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| Method | What the model does | What changes | Evidence and trade-off |
|---|---|---|---|
| Chain-of-thought prompting | Produces intermediate steps for a problem. | The prompt asks for reasoning steps or includes worked examples. | Wei and colleagues tested arithmetic, commonsense, and symbolic reasoning. In their experiments, gains appeared with larger models—around 100 billion parameters—and were greater on harder problems (paper). |
| Auto-CoT | Generates reasoning demonstrations for use as examples in later prompts. | The example set is created automatically rather than written entirely by a person. | Its authors reported matching or exceeding manual CoT on ten benchmark tasks with GPT-3; this is a result for their evaluation, not a general guarantee (paper). |
| SELF-DISCOVER | Selects and combines reasoning modules into a task-specific structure, then applies it. | The model constructs a reasoning structure for the task. | Google DeepMind reported improvements “by as much as 30%” on BigBench-Hard and Thinking4Doing, and more than 20% over inference-intensive comparisons across 24 tasks. The same publication reported using 10–40 times fewer inference compute than those comparisons. These figures are specific to the publication’s evaluation settings (publication). |
| Self-consistency | Samples several reasoning paths and selects the most consistent final answer. | Multiple inference paths are compared instead of relying on one greedy path. | Google Research reported benchmark gains of 17.9% on GSM8K, 11.0% on SVAMP, and 12.2% on AQuA under its experimental conditions; sampling additional paths uses more inference (publication). |
| Self-Refine | Generates an output, critiques it, and revises it in a loop. | One answer is iteratively edited using model-generated feedback. | A NeurIPS 2023 study across seven tasks, using GPT-3.5, ChatGPT, and GPT-4, reported roughly 20% absolute average task-performance improvement over one-step generation in its evaluations. That finding does not show that self-critique reliably fixes factual or logical errors in every setting (paper). |
| STaR | Uses generated rationales and successful answers in an iterative training-data loop. | Training is bootstrapped from a small set of rationale examples and a larger dataset without rationales. | STaR is research on building capability through a training loop, not simply a prompt-time technique (publication). |
What happens inside each approach
CoT and Auto-CoT: create intermediate steps or examples
With ordinary CoT prompting, a person can ask for intermediate steps or supply worked examples. Auto-CoT automates part of that setup: it selects diverse questions and asks a model to generate a reasoning chain for each, creating demonstrations for later prompting. The method’s authors note that generated chains can contain errors; diversity is used to reduce the harm from poor examples (Auto-CoT paper). A generated example is therefore a prompt ingredient, not a verified solution.
#1 Best Overall
SELF-DISCOVER: build a task-specific structure
Instead of sampling full solution paths or merely adding examples, SELF-DISCOVER has a model select and compose atomic reasoning modules—for example, critical thinking and step-by-step reasoning—into a structure suited to a task. That structure is then used to solve the problem. The authors describe this as a framework for tasks that challenge typical prompting methods such as CoT (Google DeepMind).
Self-consistency: compare candidate paths
Self-consistency replaces dependence on a single greedy CoT with diverse sampled paths, then aggregates their final answers by consistency. It can help when independent-looking paths converge on the same answer, but agreement is not proof: the paths may share a mistaken assumption. Its reported benchmark gains come with the practical cost of running multiple inferences (Google Research).
Rank #2
Self-Refine: critique and revise
In Self-Refine, the model first produces an output, then generates feedback on it and revises it, repeating the loop as appropriate. The study spans tasks from dialogue response generation to mathematical reasoning. This can target shortcomings in a draft, but the critique and the revision come from the same model; an articulate critique does not independently establish that its diagnosis or correction is right (NeurIPS 2023).
STaR: bootstrap training, not just a prompt
STaR (Self-Taught Reasoner) uses an iterative loop to generate rationales, retain or use successful answers, and bootstrap reasoning from a small set of rationale examples plus a larger dataset without rationales. It belongs in the same broad family of “reasoning from reasoning” ideas, but it changes training data and model capability rather than giving an end user a single prompt recipe (Google Research).
Can AI check its own reasoning?
It can attempt to review or revise its output, but that is not the same as independent verification. In a study of intrinsic self-correction—where a model tries to improve an answer using its own capabilities, without external feedback—Google DeepMind found that models struggled particularly with reasoning, and that performance could degrade after self-correction (publication). Self-review may catch some defects or improve presentation; it can also preserve or introduce errors.
The foundational CoT study illustrates why visible step-by-step text should not be treated as a guarantee. In a sample of 50 incorrect answers from LaMDA 137B examined by the authors, 46% of the chains were almost correct with minor mistakes, while 54% had major semantic or coherence errors. Those proportions describe that particular sample, not a general error rate (Wei et al.). A plausible explanation can still lead to a wrong answer, and a rationale need not faithfully record a model’s internal computation.
- For calculations, check results with arithmetic, code, or a trusted calculator.
- For factual claims, verify against reliable sources, especially when details are current or consequential.
- For decisions with material consequences, use domain expertise or an independent review rather than treating model-generated confidence as evidence.
- When trying an iterative critique, ask the model to identify assumptions and possible counterexamples, then verify any proposed correction separately.
Reasoning prompts versus reasoning models
Adding “think step by step” to a prompt is not the same as using a model trained to employ reasoning strategies. OpenAI’s o1 announcement describes reinforcement learning as helping the model hone its chain of thought and reasoning strategies; it also says users see a model-generated summary rather than the raw chain of thought (OpenAI). A displayed explanation should therefore not be assumed to be a verbatim transcript of internal computation.
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Deliberative alignment is a separate training approach: OpenAI says it teaches reasoning models to consider written safety specifications, and describes applying it to o-series models (OpenAI). This is about training models to reason with safety specifications, not a user-facing prompt technique for independently checking an answer.
Best Value
Choosing an approach for a task
- Use CoT-style steps when the task naturally breaks into a few explainable subproblems and a visible working outline is useful.
- Use automatically generated examples when repeated tasks need demonstrations, but inspect those examples before relying on them.
- Consider a composed reasoning structure when a complex task calls for an explicit organization of different reasoning moves.
- Consider sampling and aggregation when comparing several candidate solutions is worth the added inference cost.
- Use critique and revision when improving a draft is useful, while sending correctness-critical claims to an independent check.
- Distinguish training methods such as STaR from prompting methods: they address how capability is developed, not simply what a user writes in a prompt.
The published improvements above are results on named benchmarks and study tasks, not forecasts for a new model, prompt, or real-world decision. The right test is task-specific: define what counts as correct, compare against a baseline, account for extra inference, and verify outcomes independently where mistakes matter.
Quick Recap
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