AI Hallucinations: Why They Happen and How We’re Tackling Them

AI hallucinations refer to instances where a model generates a confident response that sounds plausible but is factually incorrect or entirely fabricated . For example, an AI chatbot might cite a nonexistent legal case or invent a scientific-sounding explanation out of thin air. These aren’t intentional lies – they result from the way generative AI works. In one notorious incident, an attorney submitted a court brief filled with fake citations produced by ChatGPT (which even claimed the cases were in legal databases) . Such episodes highlight why hallucinations matter: they can mislead users in high-stakes fields like law, medicine, or finance if unchecked.

Hallucination has become such a ubiquitous phenomenon that even Cambridge Dictionary’s word of the year for 2023 was “hallucinate,” specifically owing to AI’s habit of making things up. From search engines to customer service bots, AI systems today routinely produce misinformation without any intent – simply because they lack a built-in grasp of truth . As AI adoption grows (nearly half of Americans reported using AI tools in 2025 ), finding ways to rein in these fabricated answers has become one of the key challenges in AI development.

Why Do Language Models Hallucinate?

At the root of the problem is how large language models (LLMs) are trained. These models learn by predicting the next word in a sentence, based on patterns in vast text data. Crucially, they are always rewarded for producing an answer, even if they’re unsure . As OpenAI researchers succinctly put it: “We reward guessing over admitting ignorance” in today’s training regime . In other words, the AI has a strong built-in incentive to fill in the blanks with something – anything – that looks plausible, rather than saying “I don’t know.” This is why an LLM will often forge ahead and generate an authoritative-sounding but incorrect answer instead of remaining silent.

Developers are aware of this tendency and have tried to counter it during fine-tuning. Reinforcement Learning from Human Feedback (RLHF) or similar alignment techniques can teach a model to prefer saying “I’m not sure” rather than stating a falsehood. Anthropic, for instance, has trained its Claude models with an “anti-hallucination” objective so that refusal to answer is the default behavior when unsure . In practice Claude will often politely refuse a question if it doesn’t know the answer, instead of speculating. This reduces hallucinations – but it’s not foolproof. The model still has to decide when it should abstain, and that judgment can misfire.

In a recent deep-dive, Anthropic’s researchers showed exactly how these internal mechanisms can cause a model to hallucinate facts. Claude has a neural feature that acts like a “I know this” switch: when a query contains something it recognizes (say, a famous name), this feature suppresses the uncertainty reflex and lets Claude answer from its training memory . However, if the query is actually about an unknown entity and Claude only thinks it should know it, the same mechanism backfires – the model decides it must generate an answer, then makes one up to satisfy the prompt . In short, the very structure that helps an AI answer confidently when it does know something can lead it astray when it doesn’t. As one paper noted, hallucinations aren’t a mysterious quirk but a “predictable outcome” of next-word prediction plus misguided confidence triggers .

Another fundamental factor is that LLMs have no built-in fact-checker or truth gauge. They remix and regurgitate patterns from training data, which includes both truth and falsehood. They don’t truly know what’s real – they only know what words tend to follow others. As a Harvard study emphasized, these models lack the “epistemic awareness” humans have . The AI isn’t intentionally lying; it literally doesn’t have a concept of “true vs false”. Unless explicitly trained or instructed to verify facts, an LLM will treat a detailed false statement with the same fluent ease as a true one, if both sound linguistically plausible in context . This explains why even factual-sounding answers from an AI must be treated with caution – the model doesn’t know what it’s talking about, it’s just statistically stringing words together.

Techniques for Preventing Hallucinations

Reducing AI hallucinations is an active area of research, and there’s no single silver-bullet solution. Instead, researchers and developers are layering multiple strategies, from training improvements to real-time fact-checking. Here’s a look at the state-of-the-art approaches:

Better Training and Alignment: Modern AI models undergo intense fine-tuning to curb their worst instincts. Techniques like RLHF and Constitutional AI (used by Anthropic) inject human feedback or explicit principles (like “don’t fabricate information”) into the model’s objectives . The effect is that models learn during training to avoid giving answers that conflict with known data or that lack justification. OpenAI’s GPT-4, for example, was fine-tuned to be more truthful than its predecessors, and its successor was pushed even further on this front. By mid-2025, OpenAI stated that the latest ChatGPT (based on GPT-5) makes “significant advances in reducing hallucinations” – it is “significantly less likely to hallucinate” than prior models . Anthropic’s Claude 4 series similarly boasts stronger factuality checks (like demanding citations or refusing answers it can’t support) as part of being its “most aligned model yet.” These alignment measures do help: with each generation, the baseline hallucination rate has dropped on many benchmarks . However, no amount of fine-tuning can eliminate the issue entirely as long as the core architecture still guesses words – even OpenAI concedes that GPT-5’s performance “remains uneven across tasks” and some hallucinations persist .
Allowing Abstention (When Appropriate): An interesting finding is that if you permit an AI to say “I don’t know” or skip a question, its accuracy on answered questions goes way up. One OpenAI study found that when GPT-4-class models were forced to answer every question, they produced factual errors about 20–30% of the time – but when allowed to abstain on questions they were unsure about, the accuracy improved dramatically . The catch: the models then chose to refuse answering over half the questions to avoid making mistakes . This highlights a trade-off: we can have an AI that is more truthful by being cautious and often saying “I can’t answer that”, but that might not be satisfactory in practice if the user expects an answer. Still, many AI systems now incorporate the option to abstain or indicate uncertainty for borderline cases. For example, Claude or ChatGPT may preface an answer with “I’m not certain, but…” or decline extremely ambiguous queries – an approach far preferable to confidently spouting nonsense. Designers are effectively trying to calibrate the model’s confidence: encouraging it to answer only when it has a high likelihood of being correct, and to visibly flag uncertaintyotherwise .
Retrieval-Augmented Generation (RAG): One of the most powerful tools to combat hallucination is to give the LLM a lifeline to real external knowledge. Rather than relying solely on its internal memory (which might be outdated or incomplete), a model can be hooked up to a database or web search to fetch up-to-date information and facts on demand. This is how tools like Bing Chat (which uses GPT-4) and many enterprise chatbots work: the AI queries a knowledge base or the internet for relevant source material, then uses that to construct its answer. Research has shown that such retrieval-augmented systems significantly improve factual accuracy and even boost user trust in the answers . By grounding its output in documents or articles, the model is less likely to stray into fiction. OpenAI, Google, and others have all leaned into this approach. Google’s upcoming Gemini model is reportedly heavily integrated with search and factual references. Indeed, early evaluations of Gemini 2.5 showed an impressively low hallucination rate (~6% in open QA tests) compared to similar GPT-4 setups without retrieval (~15%+) . Giving AI direct access to source material helps keep it honest – though it’s not foolproof (the AI could misinterpret or misuse the retrieved info). Still, grounding responses in retrieved facts is now a standard method for cutting down hallucinations in both research and deployed systems.
Post-Processing and Verification: Another line of defense is to check the AI’s output after it’s generated but before it reaches the user. One can use a secondary model or system to detect likely false statements or to cross-verify claims. For example, researchers have developed “consistency checks” where the model is asked the same question in different ways; if it gives contradictory answers, that’s a red flag that it’s guessing. Other approaches include a “chain-of-verification” (CoVe) process: the model generates an answer, then generates a checklist of facts or sub-questions that would need to be true for the answer to hold, and then attempts to answer those . Any failure in the checklist can send the model back to revise the original answer. This essentially forces the AI to fact-check itself in a structured way. Early research indicates that such self-verification loops can reduce hallucinations, though at the cost of more computation (the model is effectively doing extra work to double-check). Similarly, companies like OpenAI and Anthropic are investing in automated monitoring that flags content that looks fabricated – e.g. an answer that includes a quote or a statistic might be passed through a filter that searches for that quote in a trusted corpus to see if it actually exists. If not, the AI might append an apology or a disclaimer.
Mechanistic Interpretability: A more futuristic approach is peering inside the “brain” of the model to understand the circuits of misinformation. The Claude study mentioned earlier is a prime example – by tracing neuron activations, researchers found distinct circuits that correlate with factual recall versus guessing . By identifying these, one could in principle tweak a model’s internals to reduce hallucination. Anthropic has built tools to toggle those circuits on and off experimentally (as they did to force Claude to wrongly claim an unknown person was a chess player) . This kind of interpretability research is still in early stages, but it promises a future where we can surgically reduce hallucinations by modifying how a model processes knowledge. OpenAI’s researchers have similarly been probing why certain questions trigger bizarre answers, hoping to find a “neural signature” of hallucination that can be fixed. While not yet yielding direct fixes, this work has already confirmed the earlier point: models don’t hallucinate out of malice or randomness, but due to identifiable components and triggers that we may eventually control.

How Do Leading Models Compare on Hallucinations?

Not all AI models are equally prone to hallucinate – and newer generations are making notable progress. It’s tricky to get apples-to-apples metrics for “hallucination rate” (since it can vary by task and measurement method), but some benchmarking efforts have shed light on the landscape:

GPT-4 vs GPT-3.5 vs GPT-5: GPT-4 (released 2023) was a big stride forward in factual reliability over GPT-3.5. On a standard truthfulness test, GPT-4’s hallucination rate was around 15–16%, whereas GPT-3.5 (the original ChatGPT) often exceeded 20% on the same tasks . OpenAI later rolled out GPT-4.5 as an intermediate update, and according to reports, it had “notably lower hallucination rates” than early GPT-4 – roughly in the 11–12% range on some evaluations . Now, GPT-5 (which by late 2025 was in limited use via ChatGPT) aims to push this further. OpenAI claims GPT-5 “hallucinates significantly less, especially in reasoning” tasks . This aligns with outside observations that GPT-5 does a better job sticking to factual responses in complex multi-step problems. However, GPT-5 is not infallible – testers note it can still confidently spit out wrong answers in areas it hasn’t been well-trained on. OpenAI’s own documentation acknowledges lingering “jagged” performance , meaning certain niche or tricky queries can trip it up. In short, each generation (GPT-3 → 4 → 5) has reduced hallucinations, but we haven’t reached zero.
Anthropic Claude 2 vs 4 (Sonnet series): Anthropic’s Claude models have a reputation for being cautious and well-aligned. Claude 2 (2023) occasionally refused to answer questions that GPT-4 might have attempted, precisely to avoid hallucinating. By 2025, Anthropic’s latest, Claude Sonnet 4.5, is touted as their “most aligned model yet” . Internal evals showed improvements in “hallucination and trustworthiness” metrics – in one red-team assessment, Claude 4.5 produced no factual errors on tested queries as long as a proper system prompt was in place . (Without any system prompt, it did have some mistakes, reinforcing the idea that prompting and guardrails matter, even for top-tier models.) Quantitatively, Claude 3.7 (an earlier version) was measured around ~16% hallucination on a broad benchmark , comparable to GPT-4. Claude 4 (and 4.5) presumably have brought that down closer to the ~10% range on similar tests, though exact figures aren’t public. The bottom line is that Claude is at least as good as GPT-4 in factuality, and Anthropic’s focus on “safer” answers means it might refuse to answer borderline questions where GPT-4 would have risked a guess . Users often report Claude feels “more careful” – a design choice aimed at minimizing hallucinated content.
Google’s Gemini: Google’s DeepMind division (which merged with Google Brain) has been working on Gemini, a next-gen foundation model intended to rival or surpass GPT-4. Although full details aren’t public as of late 2025, early versions (Gemini 2.0 and 2.5 “Pro” and “Flash” variants) have been tested. On one industry benchmark (Vectara’s FaithfulQA), Gemini 2.5 showed a remarkably low hallucination rate of about 6.3%, far outperforming contemporary GPT-4 and Claude models on the same evaluation . This suggests Google has made headway, possibly by training Gemini with even more rigorous grounding and by leveraging Google’s search/data prowess to keep it factual. Google’s CEO Demis Hassabis, however, tempered expectations by noting that today’s models (presumably including their own) “have too many holes” – they still get obvious questions wrong at times . In fact, one incident involved Google’s own Bard (an earlier model behind which Gemini tech was being integrated) citing an April Fool’s satirical article about “micro-robots” as if it were factual . So, while Gemini’s average performance might yield fewer hallucinations, it’s not immune to blunders. It’s worth noting that Google is likely using retrieval augmentation heavily with Gemini, which could account for the lower hallucination stats in benchmarks. In real-world use (like in Google’s search results), any hallucination can be glaring – so Google is striving for extreme factual accuracy. We’ll see with its official release if it truly outclasses GPT-4/5 in truthfulness across the board, but early signs are promising.
Open-Source Models (LLaMA, Mistral, Qwen, etc.): The open-source LLM community has produced many capable models, but hallucination is an even tougher nut to crack at smaller scales. Many early open models (like the first LLaMA or GPT-J) would cheerfully make up facts because they lacked the extensive fine-tuning and reinforcement that the big commercial models underwent. That said, new open models are getting better. For example, Alibaba’s Qwen series (a powerful Chinese-English model family) has undergone multiple iterations aimed at reducing hallucinations . The latest Qwen-3.5 (open-sourced in 2025) incorporates architectural tweaks and training methods to enhance factual reasoning and reduce the model’s tendency to go off-script . Developers report it is less likely to fabricate when it knows it’s uncertain. Similarly, Meta’s LLaMA 3 (hypothetical successor to LLaMA 2) is expected to put more emphasis on factual grounding. Some open models now come with built-in retrieval plugins – essentially hybrid systems that automatically search a wiki or database for you. This plugin approach helps counter hallucinations by giving the model verified info to work with. Despite these efforts, independent evaluations (like Vectara’s leaderboard) have shown open models still tend to lag a bit behind the likes of GPT-4 in factual accuracy . For instance, a 32B-parameter Qwen-2.5 model was measured around 19% hallucination rate in one test – higher than GPT-4’s ~15% on that same test . So open models are improving, but closing the gap will require more data and fine-tuning (or clever techniques) to reach the reliability of the best closed models. The advantage, however, is that being open, these models can be fully customized: a community can fine-tune a Qwen or LLaMA on a specific domain knowledge, or integrate custom retrieval tools, to dramatically lower hallucination in that niche. We’re already seeing that in specialized uses (e.g. an open medical LLM with a medical textbook database might outperform a general GPT-4 on medical factuality). This flexibility means open models can be made as truthful as needed – it just takes careful engineering.

It’s worth noting an intriguing claim made by Anthropic’s CEO: humans themselves hallucinate information quite often, perhaps more than AI does. Dario Amodei argued in 2025 that if you “really measure it,” AI models “probably hallucinate less than humans, but in more surprising ways” . Humans certainly do err and confabulate – we misremember facts, or confidently assert wrong information – but we’re generally better at knowing when we’re out of our depth. Whether AI is truly hallucinating less frequently than a person might is debatable (and as he said, it depends how you measure it ). What’s clear is that even a few percent of outputs being wrong can be a big problem when AI is used at scale. A 5% hallucination rate might sound low, but consider millions of users each getting an answer a day – that’s hundreds of thousands of incorrect outputs flowing into the world regularly. So, while progress is real, elimination is not. Leading labs all acknowledge that hallucinations still happen in their best models, and a major research goal now is driving that error rate as close to zero as possible for critical applications.

How Can Users and Developers Mitigate Hallucinations?

Given that no current AI is 100% reliable, end-users and developers have to employ strategies to manage hallucinations in real-world use. Fortunately, there are a number of practical measures one can take to significantly reduce the chance of being misled by an AI. Here are some key user-facing mitigations and their effectiveness:

Prompt Engineering – Be Specific and Demand Reasoning: One of the simplest things a user can do is craft a clearer prompt. Vague questions lead to vaguer (and often less accurate) answers. By contrast, providing context in your prompt or explicitly asking the AI to show its reasoning can expose shaky logic and prompt the model to be more careful . For example, instead of asking “What’s the cure for disease X?”, you might prompt: “Explain the established treatments for disease X and cite reputable sources.” A well-known technique is Chain-of-Thought prompting – instructing the AI to think step-by-step. Studies have shown this can improve accuracy on complex tasks by forcing the model to follow a logical path rather than jumping to an answer . If the chain-of-thought is visible, a user can also spot where the AI might be making an unfounded assumption. Another prompting tip is to set a role or persona for the AI that might make it more factual (e.g., “You are a librarian with access to a database…”). While this isn’t foolproof, it often yields answers with more factual tone and detail.
Ask for Citations or Evidence: When possible, have the AI back up its assertions. Many AI chatbots now offer cite-as-you-go styles (Bing Chat, for instance, cites web sources for factual statements). Even if using an AI that doesn’t do this automatically, you can ask, “Can you provide sources for that information?” or “How do you know this?” This serves two purposes: (1) A hallucinating AI might stumble when asked for evidence – if it’s inventing an answer, it will often either apologize and retract or it will produce obviously fake citations (e.g. journal articles that don’t exist). Either outcome alerts you that the prior answer may be dubious. (2) If it does provide a source, you as the user can then verify that source. That extra step can catch a lot of hallucinations. That said, be wary: a clever AI might even hallucinate the source! (There have been cases of chatbots fabricating reference links or mixing and matching parts of real URLs.) So always cross-check the citation if it’s not one you recognize. The act of double-checking against a trusted source (be it a website, a textbook, or an expert) is essential – never accept AI output as gospel. Remember, even OpenAI and others recommend users critically evaluate AI outputs and not rely on them alone .
Use Retrieval-Augmented Tools: As discussed, having access to external information is a game-changer for accuracy. If you’re using a large language model for an important task, consider tools or platforms that incorporate retrieval-augmented generation (RAG). This could mean using a chatbot that is connected to the web or a corporate knowledge base. Research has demonstrated that RAG not only improves factual correctness but even boosts user confidence in the answers, likely because the answers come with supporting context . For developers, frameworks like LangChain or LlamaIndex make it relatively straightforward to build an LLM application that first retrieves relevant documents (from, say, a company wiki or a medical database) and then has the model generate an answer grounded in those documents. The model effectively quotes or summarizes real data rather than relying purely on its trained internal weights. In practice, this can eliminate a huge swath of hallucinations. For example, an LLM might incorrectly recall a statistic from memory, but if it’s forced to pull the stat from a live source, it’s more likely to get it right. However, keep in mind that RAG is not magic – the retrieved documents themselves must be accurate and relevant. Garbage in, garbage out. So indexing high-quality sources is key.
Adjusting Decoding Settings (for Power Users): Many AI interfaces allow tweaking parameters like temperature. The temperature setting controls randomness in the model’s output. A high temperature (say 0.8 or 1.0) makes the model more creative and varied – which is fun for brainstorming but can induce more fabrication. For factual Q&A, using a low temperature (e.g. 0.2) will make the model more deterministic and focused on likely answers . Essentially, it will stick closer to what it believes is the single best answer, rather than getting inventive. This often means fewer hallucinations. Likewise, some applications offer an option between a “strict” mode and a “creative” mode – choose strict or precise when you need factual reliability. It may make the AI a bit more terse or conservative, but that’s usually a good trade-off for accuracy. Another decoding trick from research is contrastive decoding, where the model’s own lower layers are used to fact-check its higher layers . While not exposed to end-users directly, such techniques might be running under the hood in the next generation of chatbots, effectively damping out tokens that don’t align with a factual narrative.
Iterative Refinement and Self-Correction: If you suspect an answer might be off, you can try a technique called Reflexion (recently proposed in research) – basically, ask the model to critique and revise its output. For example: “Please double-check your answer above for any mistakes or unsupported claims.” Surprisingly often, the model will identify its own hallucinations on a second pass and correct them . It might say, “Upon review, I realize I couldn’t find evidence for X, so that part may be incorrect.” This works because the prompt nudges the model to adopt a more critical mode. Of course, this isn’t guaranteed – sometimes the model will just hallucinate a new explanation for its earlier hallucination! But engaging it in a dialog about accuracy can lead to a better outcome. Similarly, you can present the AI with a checklist or criteria: “Here is what a valid answer should include… Did your answer meet these criteria?” Prompting the model to actively verify each point can catch inconsistencies. Think of it as guiding the AI to act as its own reviewer or unit tester. Research like the Chain-of-Verification we mentioned formalizes this approach, and early evidence shows it can significantly reduce false outputs .
Employ External Fact-Checkers: If the task is mission-critical, do not rely on the model alone to verify facts. There are tools and APIs (some AI-powered themselves) that specialize in fact-checking text. For example, one could pipe the model’s answer through a service that highlights any claims and checks them against a knowledge graph or database of verified information. Developers sometimes implement a simple version of this: after the model answers, automatically run a web search for key statements and see if the top results confirm or contradict the model. As an end-user, you can emulate this by manually Googling the key facts from the AI’s answer. If the AI says “According to a 2019 Pew Research study, 54% of X…”, try searching for that study. If nothing turns up, alarm bells should ring. In essence, trust but verify – and in the absence of trust, just verify.
Maintain Contextual Boundaries: Often hallucinations occur when the model strays outside the context it was given. For example, if you were having the AI analyze a specific article and then suddenly ask a question unrelated to that article, the model might conflate unrelated knowledge. A good practice is to reset or clarify context when shifting topics, or explicitly tell the model what context to use. Developers can also enforce this by scoping the model’s knowledge – e.g., some systems will prepend “Answer only using the above document” to prevent the model from injecting outside info. By keeping the AI focused, you reduce its opportunity to hallucinate extraneous details.

Finally, an important mitigation is user education. Being aware that AI can hallucinate is half the battle. If users treat AI output with a healthy degree of skepticism – like how we (hopefully) treat random internet information – they are less likely to be led astray. Many AI applications now include disclaimers about possible inaccuracies. While these warnings can fade into the background, they serve to remind us that today’s most advanced AI is not a flawless oracle. It’s a tool that augments human effort but still requires human oversight. As one AI scientist put it, there is “no single silver bullet” for hallucinations – the best results come from combining techniques and applying common-sense verification on top .

In summary, AI hallucinations stem from fundamental aspects of how our models work, but we are making steady progress in curbing them. Through improved training, smarter prompting, grounding models in real data, and rigorous post-processing checks, the incidence of egregious fabrications is dropping. Users, too, are not powerless – by interacting thoughtfully with AI (and not expecting the impossible), we can greatly mitigate the risks. No AI is 100% hallucination-free yet, and perhaps it’s unrealistic to expect total perfection. After all, even humans err and “remember” things that never happened. The goal, however, is to make AI a lot more reliable than it is today. With each iteration – GPT-5, Claude 4.5, Gemini, Qwen 3.5, and beyond – we’re seeing meaningful improvements in truthfulness . The research community is attacking the problem from all angles, from tweaking decoding algorithms to literally opening up neural networks like microscopes on a brain. It may take a combination of all these approaches to truly solve hallucinations. Until then, the best defense is a well-informed user armed with good practices – and perhaps a trusty search engine on the side for double-checking those especially surprising “facts” the AI sometimes serves up. As the saying goes, “trust, but verify” – and nowhere is that more apt than when dealing with our brilliant yet sometimes reality-challenged AI assistants.

Key Takeaway: AI hallucinations are a complex but manageable issue. By understanding their causes and using layered solutions – from better model design to simple prompt tweaks – we can dramatically reduce their frequency. The leading models are hallucinating less often over time , and with responsible deployment (like using retrieval and encouraging uncertainty when appropriate), even user-facing AI can be made reliable for practical use. We’re not quite at the finish line, but the path to trustworthy AI is becoming clearer, one less hallucination at a time.

Sources:

Harvard Kennedy School Misinformation Review – “AI hallucinations are inaccurate outputs… that appear plausible but contain fabricated information.”
Anthropic (Claude interpretability study) – Finding that language model training incentivizes guessing, and Claude’s default is to refuse unknown answers (with a “can’t answer” circuit) until a known-topic triggers an answer . Misfiring of the “known answer” trigger leads to confabulation .
OpenAI (via Balbix blog) – “We reward guessing over admitting ignorance” – a 2025 OpenAI paper on why hallucinations occur . Also, GPT-4 models had 20–30% error rates when forced to answer everything, but improved accuracy by refusing to answer more than half the questions, highlighting the benefit of allowing abstention .
TechCrunch (May 2025) – Anthropic CEO D. Amodei claims “AI models probably hallucinate less than humans”(though in surprising ways) . Also notes Google DeepMind’s CEO saying current models “have too many holes”(obvious mistakes) and recounts a lawyer having to apologize after Claude hallucinated citations in a legal filing .
Balbix Security blog – Cites benchmarks: GPT-4 ~15.8% hallucination, Claude 3.7 ~16.0%, Google Gemini 2.5 ~6.3% on the same test (Vectara Faithful QA, 2024–25) . Also mentions OpenAI claims GPT-5 hallucinate significantly less, and Anthropic’s Claude 4 has stronger citation controls, but “both companies still acknowledge residual hallucinations… Progress is real; elimination is not.”
Galileo AI (Exploring Qwen) – Notes that Alibaba’s Qwen 3.5 open-source models include architectural improvements to reduce hallucinations and improve instruction-following .
MIT Sloan EdTech – Advice on mitigating hallucinations: “Research has shown RAG improves factual accuracy and user trust” ; “Chain-of-Thought prompting… improves transparency and accuracy” ; using clear prompts and low temperature yields more factual responses . Also emphasizes double-checking AI content with outside sources or experts .
Analytics Vidhya (Sept 2025) – Comprehensive overview of anti-hallucination techniques. Key takeaways: no single solution; best to combine methods (prompting, reasoning, RAG, verification) , and when in doubt or high-stakes, the model should abstain or signal uncertainty . Describes advanced methods like ReAct, Tree-of-Thought, Reflexion, Chain-of-Verification, and new decoding algorithms that have shown promise in cutting down hallucinations .