Hallucinations in LLMs: Challenges and Prompt Engineering Solutions (2024-2025) {#hallucinations-in-llms-challenges-and-prompt-engineering-solutions-2024-2025}
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Hallucinations in LLMs: Definitions and Types
Prompt Engineering Techniques to Control Hallucinations
Highlights from Recent Research (2024–2025) on Hallucination Mitigation
Mitigating Hallucinations in Document Digitization Pipelines
Conclusion
Hallucinations in LLMs: Definitions and Types {#hallucinations-in-llms-definitions-and-types}
Hallucination in large language models (LLMs) refers to the generation of content that is unfaithful to the source or factual reality – in other words, the model produces information that is not supported by its input or by verified knowledge (Valuable Hallucinations: Realizable Non-Realistic Propositions). These outputs may appear plausible or confident but are factually incorrect, fabricated, or irrelevant to the given context (HERE). Researchers commonly distinguish between:
Intrinsic vs. Extrinsic Hallucinations: Intrinsic hallucinations occur when an LLM’s output contradicts or misinterprets the provided input (e.g. the model’s answer conflicts with the source document or user query). Extrinsic hallucinations occur when the model introduces information that goes beyond the input and cannot be verified by it . In document-based tasks, an intrinsic hallucination might be a summary that distorts a document’s content, while an extrinsic hallucination might inject facts not present in the document.
Factual (Knowledge) Hallucinations: Many recent works focus on factual inaccuracies, where the model confidently states incorrect “facts” or data. For example, an LLM might invent a non-existent detail about a document’s content. These factuality errors have become a primary concern in LLM deployments (HERE). Other taxonomies further break down hallucinations by the nature of the error – e.g. incorrect entities or dates, unsupported claims, outdated information, or logically inconsistent statements – all of which undermine the model’s reliability.
Hallucinations make LLMs untrustworthy for high-stakes applications, since users may be misled by confident but incorrect answers (HERE). Addressing this issue is critical for domains like law, finance, or medicine where accuracy is paramount (HERE). The remainder of this review focuses on techniques, especially prompt engineering methods, to control and reduce hallucinations in LLM outputs.
Prompt Engineering Techniques to Control Hallucinations {#prompt-engineering-techniques-to-control-hallucinations}
Prompt engineering – crafting the instructions or context given to an LLM – has emerged as a key lever for steering models towards more truthful and grounded outputs ( Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models). Recent studies (2024–2025) show that how we prompt an LLM can significantly influence its tendency to hallucinate. Below we outline techniques that have been effective in controlling hallucinations:
Retrieval-Augmented Prompting (RAG): One of the most effective ways to curb hallucinations is to ground the LLM in actual reference text. In a retrieval-augmented generation setup, the model is first provided with relevant documents or chunks retrieved from a knowledge base, and then asked to formulate an answer using that information. By feeding the model factual context, it is less likely to “make up” content. Indeed, RAG has become a primary technique for mitigating hallucinations, as it forces the model to base its output on retrieved evidence rather than unsupported guesses (HERE). An important aspect here is document chunking: breaking a long document into semantically coherent chunks that can be embedded and retrieved. Proper chunking is “a key factor in the success of RAG” because it ensures that relevant information can be found and supplied to the prompt with minimal noise . In practice, this means segmenting documents (by sections, paragraphs, etc.) so that the LLM always has the most pertinent text at hand, reducing chances of drifting off-topic.
Detailed and Context-Rich Instructions: The wording and structure of the prompt itself can influence hallucination rates. Empirical evidence shows that including more detail in the task description and providing clear context or examples (in-context learning) leads to fewer hallucinations (HERE). For instance, instead of a vague prompt like “Summarize this document,” a more specific prompt could be: “Using only the information in the text above, provide a summary. If details are missing in the text, do not speculate.” Such explicit instructions and even few-shot examples of proper behavior help the model stay truthful. Conversely, poorly designed prompts can induce mistakes – e.g. placing the question before a long instruction or phrasing the query ambiguously has been found to increase hallucination occurrence . The takeaway is that prompts should be clear, specific, and constrained to the provided sources, which guides the model to focus on given facts.
Chain-of-Thought and Step-by-Step Reasoning: Prompting the model to explain its reasoning or break the task into steps (Chain-of-Thought prompting) can improve faithfulness. By having the model generate intermediate reasoning steps, we force it to confront contradictions and self-check its logic before giving a final answer ( Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models). For example, in a QA on a document, the model might first list relevant facts from the text, then draw a conclusion. This process can make errors more apparent and reduce leaps of faith. One caveat noted in research is that models don’t always follow format instructions perfectly, which can lead to other issues (e.g. formatting errors ). Still, when applied correctly, chain-of-thought prompting often helps in complex tasks by structuring the model’s thinking, thereby lowering the chance of hallucinating details that would be caught in a step-by-step analysis.
Self-Consistency and Majority Voting: Even with a fixed prompt, LLMs might produce varying answers on different runs (due to their probabilistic nature). The Self-Consistency technique generates multiple answers by sampling the model several times, then uses a majority vote or confidence measure to select the most consistent answer . The intuition is that false or random hallucinations will appear in some outputs but the truthful answer (being grounded in the text) should appear more frequently. Barkley and van der Merwe (2024) observed that even simple prompting combined with such repeated sampling can outperform more complex prompting methods in reducing hallucinations ( Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models). In practice, this might mean posing the same question multiple times (with slight prompt variations or different random seeds) and taking the answer that most often recurs, under the assumption that hallucinations are less likely to consistently repeat.
Reflective Prompting and Verification: Another powerful prompt-based approach is to have the model critique or verify its own answer. In a reflection strategy, after the model produces an answer, we append a follow-up prompt like: “Check the above answer against the source document and list any errors or unsupported statements.” The model (or a second instance of an LLM) then evaluates the first answer and can be prompted to correct any issues ( Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models). This kind of iterative refinement has been shown to reduce factual errors. For example, one 2025 study introduced a “Chain-of-Verification (CoVe)” where the model generates a verification question based on its initial answer, answers that question, and compares it to the original answer – if there’s a contradiction, it refrains from giving a final answer . Such prompt-guided verification loops effectively catch discrepancies. Researchers demonstrated that prompting a model to reflect and then revise can significantly cut down hallucinations, steering the model to either correct the mistake or abstain from answering if unsure (Valuable Hallucinations: Realizable Non-Realistic Propositions).
Constrained Output Formats: Prompting the model in a way that restricts its output can also help. For instance, using templates or asking for answers in a structured format (bullet points, JSON with fields, etc.) leaves less room for the model to go off-script. Some prompt frameworks include a “do not know” option explicitly – e.g. “Answer with the relevant fact from the text, or say ‘Not found in document’ if the text doesn’t mention it.” By encouraging the model to admit uncertainty or choose a fallback response rather than fabricate, we can avoid many hallucinations. In experiments with contradiction penalization prompts, if a model’s multiple sampled answers disagreed with each other, the strategy was to have the model output no answer at all to prevent a likely hallucination . Real-world implementations similarly often configure prompts or system messages to prefer silence or a neutral response over a conjecture when the evidence is insufficient.
It’s important to note that the optimal prompting technique can depend on the task and model. A comprehensive evaluation in late 2024 found that no single prompting method wins universally: simpler prompts (e.g. basic instructions with retrieval) sometimes outperformed complex multi-step frameworks on factual QA, whereas for reasoning tasks, chain-of-thought or self-consistency had more benefit ( Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models). Overall, prompt engineering provides a toolkit of strategies – from adding relevant context, crafting clearer instructions, to multi-step self-checking – that guide LLMs toward more faithful outputs.
Highlights from Recent Research (2024–2025) on Hallucination Mitigation {#highlights-from-recent-research-2024-2025-on-hallucination-mitigation}
Recent arXiv papers have advanced our understanding of hallucinations and how to reduce them in LLMs. Key findings include:
Barkley & van der Merwe (2024) – “Investigating the Role of Prompting and External Tools in Hallucination Rates of LLMs.” This empirical study tested various prompting strategies (chain-of-thought, self-consistency, debate between models, tool use, etc.) across benchmarks. The authors found that simple prompting techniques often rival or beat more elaborate ones in reducing hallucinations, and that adding external tool usage (LLM agents) can sometimes increase hallucination rates due to the added complexity ( Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models). Their work underscores that prompt design should be as straightforward as possible for a given task, and that each problem may require a tailored approach.
Sun et al. (2024) – “An Empirical Study of LLM Hallucination Sources and Mitigation” (analysis of pre-training, fine-tuning, and prompts). This study provided a fine-grained look at what causes hallucinations. Pertinent to prompt engineering, they showed that prompts with richer detail and more formal, specific language yield fewer hallucinations, while confusing or overly complex prompts lead to more errors (HERE). They also noted that models are less likely to hallucinate on queries that are easy to read and clearly stated . This suggests that investing effort in prompt clarity (and possibly simplifying the user’s question) can pay off in reliability.
Eghbali & Pradel (2024) – “De-Hallucinator: Mitigating LLM Hallucinations in Code Generation via Iterative Grounding.” This work tackled hallucination in the context of code generation (where models often invent nonexistent API calls). The proposed solution retrieved relevant API documentation based on an initial attempt from the LLM and iteratively re-prompted the model with these references, effectively grounding the model in correct information (De-Hallucinator: Mitigating LLM Hallucinations in Code Generation Tasks via Iterative Grounding) . This retrieval and re-query loop dramatically improved factual accuracy in code outputs (e.g. increasing correct API usage by 24–61% in their experiments) . The principle of iterative grounding can be generalized to natural language tasks – using the model’s first output as a clue to fetch missing info, then querying again with that info to fix mistakes.
Wang et al. (2024) – “Developing a Reliable Hallucination Detection and Mitigation Service.” Researchers at Microsoft described a real-world system for catching and correcting hallucinations in enterprise LLM applications. They combined multiple detectors (e.g. NER-based checks, NLI for fact consistency, etc.) to flag potential hallucinations, and then fed the model a dynamically generated prompt to rewrite its answer to eliminate errors (HERE). This iterative refinement loop (re-prompting the LLM to fix its own mistakes) continues until no hallucinations are detected. Their production deployment showed this approach can reliably rectify hallucinated answers with minimal human intervention, as long as the model is guided by targeted prompts that highlight what needs correction . It demonstrates an important implementation strategy: using the LLM itself as a repair tool when given appropriate feedback via prompts.
Song et al. (2025) – “Valuable Hallucinations: Realizable Non-Realistic Propositions.” While most studies aim to eliminate hallucinations, this paper took a different perspective. It formally defined valuable hallucinations (creative ideas that are not factual but could inspire useful solutions) and explored how to use prompt engineering to control the type of hallucinations (Valuable Hallucinations: Realizable Non-Realistic Propositions) . By using reflection prompts and careful instruction, the authors managed to increase the proportion of these “creative” (but benign) hallucinations while filtering out the harmful ones . This highlights that prompt engineering can not only suppress hallucinations but also shape the nature of any that do appear, aligning them with user goals (e.g. brainstorming) when absolute factual accuracy is less critical.
Mitigating Hallucinations in Document Digitization Pipelines {#mitigating-hallucinations-in-document-digitization-pipelines}
In practical document processing workflows – such as digitizing PDFs and using LLMs to answer questions or summarize – controlling hallucination is crucial. Document digitization often involves OCR and chunking of a long text, followed by feeding those chunks to an LLM. Several strategies from the latest research can be applied in this context:
Intelligent Chunking and Retrieval: As mentioned, how you split a document can make or break factual accuracy. It’s important to chunk documents in a semantically meaningful way (e.g. by sections or topics) and use an embedding-based retriever to fetch the most relevant chunk for a given query (HERE). When a user asks a question, the system should retrieve the specific document segments that likely contain the answer, and include only those segments in the LLM’s prompt. This minimizes the chance that the model’s answer strays beyond the provided content. Recent work emphasizes chunking by document structure (headings, paragraphs, etc.) to preserve context . By ensuring the model always has the correct chunk at hand, we prevent it from being “lost in the middle” of a long document and guessing at answers (HERE). In essence, proper chunking + retrieval is a form of prompt engineering – it constructs a prompt that contains the relevant factual material, thereby grounding the model’s response.
Source-aware Prompting: When prompting the model with retrieved document text, it’s effective to explicitly instruct the model to stick to the source. For example: “Answer based only on the text below. If the answer isn’t in the text, say you don’t know.” This kind of prompt acts as a safeguard against hallucination by telling the LLM that we prefer ignorance to invention. While an LLM might still hallucinate, the prompt bias is toward using the given evidence or refusing to answer. In a 2024 study, a “contradiction-aware” prompting method was used where if the model found its answers conflicting, it would rather output nothing than risk a false answer ( Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models) – a strategy that mirrors what we want in document QA (better to say “Not found in the document” than to fabricate a plausible-sounding fact). Many real-world QA systems now include a variant of this approach, often implementing it as a system or chain-of-thought prompt that at the end checks: “Is my answer fully supported by the document? If not, revise or abstain.”
Multi-step Verification: In a document pipeline, after the LLM produces an answer, an additional verification step can be inserted. For instance, one can prompt the LLM (or a second model) with “Given the document text and the answer, is the answer correct and supported? If not, what is wrong?” If the verifier identifies a hallucination (e.g. it finds a statement in the answer that doesn’t appear in the source), the system can either refuse to deliver that answer or ask the LLM to try again with more focus. This approach was effectively used in the Microsoft system, where the model was prompted to fix any unsupported portions of its output (HERE). Similarly, the Contrato360 Q&A application (2024) for contract documents used a combination of retrieval, tools, and prompt-based agent orchestration to ensure answers are accurate; they report that such multi-agent prompting “significantly improve the relevance and accuracy” of answers in a document-heavy workflow (HERE). The key is that every answer is cross-checked against the source material via some prompt-driven logic before being finalized.
Continuous Improvement with Feedback: A deployed document AI system can log cases where the LLM output was wrong or flagged by users, and these can be turned into new prompt tweaks or few-shot examples. For example, if the model tended to hallucinate certain bibliographic details in summaries, one can add a demonstration in the prompt of a summary that says “(Unknown)” for missing info. Over time, the prompt (or an external instruction dataset) is refined to plug habitual holes. Recent research suggests that fine-tuning with enhanced instructions or feedback can further reduce hallucinations (HERE), but even without model retraining, iteratively improving the prompt based on failure cases is a practical strategy.
In summary, document digitization pipelines today rely on a synergy of retrieval and prompt engineering to tackle hallucinations. By feeding the right context, using prompts that discourage fabrication, and sometimes having the model double-check itself, we can greatly increase the reliability of LLM outputs on digitized documents. These measures, informed by the latest research, make it feasible to deploy LLMs in settings like contract analysis, reporting, and enterprise search, where users can trust that the answer truly comes from the document and not the model’s imagination.
Conclusion {#conclusion}
Hallucination in LLMs remains a significant challenge, but the rapid progress in 2024–2025 shows that we are developing effective controls. A combination of prompt engineering techniques – from grounding the model in retrieved document chunks, to structuring prompts and output formats carefully, to leveraging the model’s own reasoning and self-checking abilities – can substantially mitigate hallucinations in practical applications. Importantly, studies indicate that hallucinations cannot be fully eliminated due to the probabilistic nature and training limitations of LLMs (Valuable Hallucinations: Realizable Non-Realistic Propositions). However, by applying the strategies highlighted above, we can reduce their frequency and impact, leading to more factual and faithful AI-generated content. For document processing and other knowledge-intensive tasks, these prompt-based interventions, coupled with ongoing research insights, pave the way for LLMs to become trustworthy assistants rather than confabulation-prone storytellers.
Sources: Recent arXiv papers from 2024–2025 on LLM hallucination and mitigation (HERE)