Enhancing ChatGPT Outputs: A Framework for Reliable and Detailed Responses Through Advanced Customization

Executive Summary

Large Language Models (LLMs) such as ChatGPT are increasingly integrated into diverse applications, yet their utility is often constrained by variability in output reliability and detail. This report examines the mechanisms available for tailoring ChatGPT's behavior to address these challenges, focusing on prompt engineering techniques, custom instructions, and API parameter tuning. A central observation is that while advanced prompting strategies can boost factual accuracy, they may inadvertently inflate the model's confidence in its responses, highlighting a critical distinction between correctness and calibrated certainty. Furthermore, achieving comprehensive and precise detail necessitates highly granular instructions and an iterative refinement process.

Key findings indicate that foundational system messages establish a persistent behavioral framework for the AI, while nuanced parameter selection, particularly temperature, requires careful consideration beyond simple minimization for factual tasks. Prompt engineering functions as a form of cognitive guidance, structuring the model's internal processing to yield more robust results. The report synthesizes these principles into a practical customization framework, emphasizing the dynamic and iterative nature of optimizing LLM performance. Recommendations include adopting a multi-faceted approach to customization, prioritizing specificity in instructions, leveraging advanced prompting techniques like Chain-of-Thought and Few-Shot learning, and maintaining continuous evaluation to mitigate inherent LLM limitations such as hallucinations.

Introduction: The Imperative of Reliable and Detailed LLM Outputs

The integration of Large Language Models (LLMs) like ChatGPT into various professional and personal applications is rapidly expanding, transforming workflows from content generation to complex decision-making support. These models offer unprecedented capabilities in processing and generating human-like text, making them invaluable tools across numerous domains. However, a common challenge encountered by users is the variability in the quality of LLM outputs, frequently manifesting as unreliable or insufficiently detailed answers, despite the models' advanced general capabilities.

To fully leverage the potential of LLMs, it becomes critical to define and achieve "better" outputs. In this context, "better" encompasses two primary dimensions:

• Reliability: This refers to the factual accuracy, internal consistency, and the absence of fabricated or misleading information, often termed "hallucinations". For high-stakes applications, such as those in medical or legal fields, reliability also involves the model's calibrated confidence—its self-assessed certainty aligning with actual correctness.

• Detail: This dimension pertains to the comprehensiveness, depth, and adherence to specific output requirements, ensuring that responses are not only accurate but also sufficiently thorough and structured for the user's purpose.

The primary avenue for guiding LLM behavior without undergoing extensive model retraining is through strategic customization, commonly referred to as prompt engineering. OpenAI, the developer of ChatGPT, explicitly supports such customization through its API parameters and user-facing custom instructions, providing developers and users with tools to fine-tune model responses. This report will explore these customization methods to provide a comprehensive framework for eliciting more reliable and detailed outputs from ChatGPT.

Understanding ChatGPTs Customization Landscape

Tailoring ChatGPT's behavior and outputs involves several distinct, yet interconnected, mechanisms. These range from foundational settings that define the AI's core persona to granular controls over specific output characteristics.

System Prompts and Custom Instructions: Foundations of Behavioral Control

At the core of guiding ChatGPT's responses are system prompts and custom instructions, which establish the fundamental parameters for the AI's interactions.

System Prompts (API-level): These are foundational messages provided to the AI model, typically through an API, that define its role, personality, and general behavioral guidelines. They act as "hidden instructions telling the AI how to behave," setting a persistent context for the entire conversation or application session. For instance, a system message can instruct the AI to act as an "expert medical researcher providing evidence-based answers" and to "always cite sources if asked for factual claims". This mechanism is crucial for ensuring consistency in the AI's persona, tone, and adherence to predefined rules across multiple interactions. It allows developers to set boundaries, specifying what the AI should and should not answer, thereby preventing undesirable outputs or topic drift.

The influence of system messages extends beyond a single interaction, establishing a deep, persistent influence on the model's underlying behavioral paradigm. This suggests that system messages create an "implicit contract" or a meta-instruction layer that shapes the AI's entire operational context. By defining the AI's core identity and guardrails at this fundamental level, the need for constant re-prompting for basic behavioral adherence is minimized, and the likelihood of unexpected or off-topic responses is significantly reduced. This foundational layer of customization is more fundamental than individual prompt engineering for specific queries, providing a stable base for more refined instructions.

Custom Instructions (ChatGPT User Interface): Distinct from API-level system prompts, custom instructions are a user-facing feature within the ChatGPT interface that allows individual users to set persistent preferences for how ChatGPT should respond across new chats. This feature is a convenient way for users to embed their preferred output style, format, or persona without the "tedious" repetition of these characteristics in every prompt. For example, a user can specify a desired output length, a particular tone, or require the inclusion of specific elements like SEO keywords or FAQ sections in all generated content. These instructions are applied to new conversations, meaning existing chat threads will not retroactively adopt them.

API Parameters: Granular Control Over Output Characteristics

Beyond explicit instructions, several API parameters offer granular control over the model's output generation process, significantly influencing reliability and detail.

• Model Selection: The choice of the underlying LLM model (e.g., GPT-4o versus GPT-3.5-turbo) is a primary determinant of performance, cost, and latency. Generally, higher-performance models are more capable of generating detailed and reliable responses, though they typically come with higher computational costs and potentially increased latency.

• Temperature: This is a critical parameter that controls the randomness and creativity of the model's output. A higher temperature setting leads to more random and creative outputs, while a lower temperature makes the output more deterministic and focused on the most probable tokens. For most factual use cases, such as data extraction and truthful question-answering, a temperature of 0 is often recommended to maximize determinism and reduce variability.

However, the optimal temperature for reliability is not a universal constant of zero. Research indicates that for complex factual reasoning tasks, particularly in high-stakes fields like medicine where confidence calibration is crucial, a slightly higher, yet still conservative, temperature (e.g., 0.3–0.7) might yield more balanced gains in accuracy and the alignment of confidence estimates with empirical evidence, especially when combined with advanced prompting strategies like few-shot learning. A temperature of 0 might make the model too deterministic, potentially limiting its exploration of alternative reasoning paths that could lead to more calibrated or accurate answers in certain complex scenarios. Conversely, high temperatures (e.g., 1.0) can lead to detrimental overconfidence, where the model expresses high certainty even when incorrect. This suggests a need for empirical tuning of temperature in conjunction with specific prompt types to achieve true reliability.

• Max Completion Tokens: This parameter sets a hard cutoff limit for the maximum number of tokens the model can generate in a single response. While it does not directly control the desired length, it acts as a safeguard to prevent overly long or runaway generations. Ideally, the model should stop naturally when it deems its response complete or encounters a defined stop sequence. This parameter is vital for controlling verbosity and ensuring that responses remain focused and within manageable limits, contributing to the perceived detail and utility of the output.

• Stop Sequences: These are specific characters or tokens that, when generated by the model, will cause the text generation to cease. Stop sequences are highly useful for programmatically halting generation at a precise point, especially when structured outputs are desired. This enhances control over the output format and ensures the model does not generate content beyond the intended scope or structure.

Core Principles of Advanced Prompt Engineering for Enhanced Performance

Crafting effective prompts is fundamental to eliciting reliable and detailed responses from LLMs. This involves structuring instructions, providing clear guidance, and leveraging specific techniques to guide the model's generation process.

Structuring Prompts for Clarity and Specificity

Effective prompt engineering begins with clear and unambiguous instructions. A best practice involves placing instructions at the beginning of the prompt, often separated from the main context using delimiters such as ### or """. This ensures that the model prioritizes the instructions before processing the contextual information, leading to more focused and compliant outputs.

Furthermore, prompts must be "specific, descriptive and as detailed as possible about the desired context, outcome, length, format, style, etc.". Vague or unclear input can confuse the model, leading to irrelevant or inaccurate responses. For example, instead of a general request like "Summarize this text," a more effective prompt would be: "Summarize the text below as a bullet point list of the most important points. Text: """[text input here]""". This level of specificity directly impacts both the detail and reliability of the output by reducing ambiguity and guiding the model towards the precise information and presentation required.

The power of examples, often referred to as Few-Shot Prompting, is another critical technique. Articulating the desired output format or behavior through concrete examples is highly effective. Few-shot prompting provides the model with instances of the desired input-output mapping, significantly improving its ability to generalize and produce accurate, structured, and detailed responses. This method can lead to "more balanced gains in accuracy and calibration," making it crucial for enhancing reliability.

The manner in which instructions are framed also plays a significant role. It is advisable to frame instructions positively, telling the model what to do rather than only what not to do. Positive instructions are generally clearer and more actionable for the model, reducing the likelihood of misinterpretations. Additionally, for tasks like code generation or highly structured outputs, using "leading words" can subtly nudge the model towards a particular pattern or syntax, thereby improving accuracy and adherence to format.

These prompt engineering techniques—specificity, instruction placement, and the use of examples—do not merely tell the model what to output, but rather guide how it should process information or "think." This is analogous to providing "cognitive scaffolding" for the LLM, structuring its internal inference process rather than just its final generative act. For instance, Chain-of-Thought (CoT) prompting explicitly guides step-by-step reasoning, while Few-Shot examples provide concrete processing templates. This approach helps the model construct a more robust internal representation of the problem and its solution, making its reasoning more explicit and less prone to unreliability. This is particularly critical for complex or high-stakes tasks where internal consistency and traceable logic are paramount.

Strategies for Mitigating Unreliability: Addressing Hallucinations and Inaccuracies

A significant challenge in achieving reliable LLM outputs is the phenomenon of "hallucinations," where models generate information that is plausible but factually incorrect or nonsensical. Understanding the causes and implementing specific prompting techniques are crucial for mitigation.

Deconstructing LLM Hallucinations: Causes and Manifestations

LLM hallucinations occur when the model produces "misleading or incorrect information," "fabricated historical events, scientific facts, or biographical details," or "unusual or nonsensical statements," often presented with high confidence as if factual.

The root causes of hallucinations are multifaceted:

• Probabilistic Nature: LLMs operate by predicting the most likely next word or token based on patterns learned from vast datasets, rather than by verifying facts against a real-world knowledge base. This probabilistic generation can lead to outputs that are linguistically plausible but factually unsound.

• Training Data Limitations: Hallucinations often stem from inaccuracies, biases, incompleteness, or outdated information within the training data. Models may overfit by memorizing specific training patterns rather than generalizing knowledge, or underfit by generalizing too broadly in complex cases, both leading to inaccuracies. For instance, ChatGPT's free version was last trained on data up to January 2022, limiting its knowledge of recent events and increasing the risk of outdated information.

• Model Architecture Limitations: Factors such as limited context windows (the fixed number of tokens the model can process simultaneously), weak attention mechanisms (how effectively the model weighs relevant information), and tokenization issues can contribute to misinterpretation and inaccuracies.

• Prompt Issues: Ambiguous or overly complex prompts can confuse the model, leading it to generate unrelated facts or misinterpret instructions, thereby increasing the likelihood of errors.

Hallucinations can manifest in various ways, including factual inaccuracies, logical inconsistencies within a response, overly vague or excessively detailed responses lacking nuance, unusual or nonsensical statements, inherent biases, overgeneralization, and the fabrication of non-existent citations or data.

Prompting Techniques for Factual Consistency

Several prompting techniques can significantly enhance factual consistency and mitigate hallucinations:

• Chain-of-Thought (CoT) Prompting: This technique guides the LLM to undertake step-by-step reasoning, explicitly showing its intermediate thought processes before arriving at a final answer. CoT consistently boosts accuracy by making the model's reasoning transparent, which can aid in identifying and correcting errors. However, it is important to note that while CoT improves accuracy, it can also heighten "overconfidence" in erroneous outputs, meaning the model might sound very certain even when incorrect. This necessitates careful evaluation and potentially post-hoc calibration of confidence.

• Expert Mimicry Prompting: This strategy involves instructing the LLM to adopt the perspective of a knowledgeable expert in a specific domain. By leveraging the model's ability to embody a persona, this approach can increase absolute accuracy by guiding the model to access and present information consistent with that expertise. Similar to CoT, expert mimicry may also exacerbate overconfidence.

• Few-Shot Prompting: As discussed previously, providing concrete examples of desired input-output pairs helps shape the model's expected responses and can align its confidence estimates more closely with empirical evidence. This technique produces "more balanced gains in accuracy and calibration" under conservative temperature settings, making it crucial for enhancing overall reliability by addressing both correctness and the model's self-assessment of its correctness.

• Hybrid Approaches: Combining techniques, such as CoT and Few-Shot prompting, can leverage the benefits of both, allowing for extensive reasoning while referencing example outputs and justifications.

The Interplay of Model Limitations and Prompt Design

While prompt engineering offers substantial advantages, such as not requiring costly labeled datasets and being less resource-intensive than fine-tuning, it may not consistently outperform models that have been fine-tuned on specific, task-oriented data. Fine-tuning involves modifying the pre-trained model's parameters to optimize for a particular task, often achieving better performance but demanding more data and computational resources.

Crucially, the effectiveness of prompt engineering is significantly enhanced by human involvement and iterative refinement. Studies show that "conversational prompts, incorporating human feedback during interaction, significantly improved performance" compared to fully automated prompting. This underscores that for maximizing reliability and detail, a "set-and-forget" approach to customization scripts is insufficient. Continuous human oversight, evaluation of outputs, and refinement of prompts based on observed performance are essential.

A critical observation across various studies is the "confidence-accuracy disconnect" in LLMs. While advanced prompting strategies, particularly Chain-of-Thought, can boost factual accuracy, they frequently inflate the model's confidence in both correct and incorrect outputs. Research explicitly states that "accuracy enhancements in medical LLMs do not inherently translate into reliable uncertainty assessments," and that current confidence elicitation methods remain unreliable for gauging LLM knowledge and uncertainty. This means that a highly confident-sounding answer from an LLM is not necessarily a reliable one, especially in high-stakes fields. Therefore, simply aiming for higher accuracy through prompt engineering is insufficient for true reliability; it is equally important to implement strategies that encourage calibrated confidence or to utilize post-hoc calibration methods, or for the prompt script to instruct the model to explicitly flag uncertainty.

Techniques for Maximizing Detail and Comprehensiveness

Ensuring ChatGPT provides sufficiently detailed and comprehensive responses involves guiding its output length, depth, and adherence to specific content and format requirements.

Guiding Output Length and Depth through Prompt Constraints

To control the verbosity and depth of the AI's responses, explicit instructions regarding length and scope are essential. This includes specifying desired word counts, sentence counts, or paragraph counts. Such direct constraints ensure that the output meets specific length criteria, contributing directly to the perceived detail and preventing overly brief or superficial answers.

Furthermore, clearly defining the scope and breadth of the response is critical. This involves explicitly stating the topics to be covered, the number of points to include, or the level of granularity required. For example, prompts can specify to "include [number] practical tips," "cover [number] current statistics," or "highlight [number] technical skills". These instructions ensure comprehensive coverage of the subject matter, preventing the omission of critical details and guiding the model to explore the topic to the desired depth.

Articulating Desired Formats and Content Requirements

The utility of detailed information is significantly enhanced when it is presented in a clear and organized manner. Therefore, specifying structured formats is highly effective. This includes requesting outputs in forms such as bullet points, numbered lists, tables, outlines, or specific code structures. For instance, data analysts often benefit from responses formatted into tables or bullet points, which are key customization options. Structured outputs are inherently more detailed and easier for users to parse and utilize.

Additionally, explicitly requesting a specific tone (e.g., formal, analytical, humorous, professional) or writing style ensures that the detail is presented in a manner appropriate for the target audience and context. Requiring the inclusion of specific elements, such as keywords, meta-descriptions, FAQs, or particular sections, guarantees that essential details pertinent to the task (e.g., SEO for content creation) are present in the output.

Iterative Refinement and Human Feedback in Prompt Development

Prompt engineering is an inherently iterative process. Initial prompts may not always yield optimal results, necessitating refinement based on the evaluation of the generated outputs. Research findings indicate that incorporating human feedback during interaction with LLMs significantly improves performance. This highlights that for maximizing both detail and reliability, a "set-and-forget" approach to customization scripts is insufficient. Ongoing testing, evaluation, and adjustment of prompts are crucial.

The pursuit of maximal and appropriate detail is not simply about adding more instructions to a prompt; it is fundamentally about establishing a feedback loop. The initial prompt provides granular constraints, and subsequent human evaluation identifies areas where detail might be lacking, excessive, or misdirected. This iterative process then informs further prompt refinement, ensuring that the model's outputs align precisely with the user's expectations for comprehensiveness. This implies that a "better customization script" is not a static artifact but rather a living document, continuously optimized through human review of the generated outputs.

Developing an Optimized Customization Script: A Practical Example

Synthesizing the principles discussed, an optimized customization script for ChatGPT adopts a multi-layered approach, combining foundational behavioral guidelines with task-specific instructions and parameter tuning.

Synthesizing Best Practices into a Cohesive Script Structure

A robust customization script should integrate system prompts (for API use) or custom instructions (for the ChatGPT user interface) to establish a core persona and behavioral rules. This is then complemented by specific user prompts that provide task-specific instructions and context. Clarity and delimitation are paramount, using clear separators like ### or """ to distinguish instructions, context, and examples. Assigning a specific role or persona to the AI (e.g., "expert medical researcher") guides its knowledge domain and tone. Explicit constraints for length, format, and content should be incorporated. Finally, instructions for reliability, such as encouraging step-by-step reasoning (Chain-of-Thought) or referencing examples (Few-Shot), and guidance on handling uncertainty, are critical.

Example Customization Script for Reliable and Detailed Responses

Consider the scenario of an AI Assistant designed for Technical Report Generation, specifically in the biomedical research domain.

Custom Instructions (for ChatGPT User Interface):

• Box 1 (What you want ChatGPT to know about you): "I am a Biomedical Researcher specializing in oncology and drug discovery. My primary goal is to obtain highly reliable, evidence-based, and detailed scientific information for academic publications and clinical decision support. I value factual accuracy and comprehensive explanations over brevity or creative interpretation. My work requires precise, verifiable data."

• Box 2 (How you want ChatGPT to respond): "As a highly reliable AI assistant for biomedical research, you must adhere to the following principles for all responses:

1. Prioritize Factual Accuracy: Cross-reference information internally and, if possible, indicate potential sources or data limitations. Absolutely avoid speculation, fabrication, or 'hallucinations'. If information is uncertain or beyond your knowledge cutoff, explicitly state this.

2. Detailed and Comprehensive Responses: Provide exhaustive answers, covering all relevant sub-topics and nuances. Aim for a depth suitable for a graduate-level academic audience, ensuring all facets of the query are addressed thoroughly.

3. Structured Output: Always present information in a clear, logical, and structured format. Use numbered lists, bullet points, tables, or well-organized paragraphs with distinct headings and subheadings.

4. Step-by-Step Reasoning (Chain-of-Thought): For complex analytical questions, break down your reasoning process into explicit, sequential steps before providing the final answer. This enhances transparency and allows for verification of the logical flow.

5. Cite Information (if applicable): If specific data points, studies, or concepts are discussed, suggest where such information could be verified (e.g., 'Refer to peer-reviewed journals on...', 'Clinical trial data available from...'). Do not fabricate citations.

6. Concise Language, but Not Superficial: Use precise, academic, and unambiguous language. Avoid 'fluffy' or imprecise descriptions.

7. Maintain Professional Tone: Your tone should be objective, formal, and authoritative, reflecting an expert in the biomedical field.

8. No Conversational Fillers: Get straight to the point. Do not ask follow-up questions unless explicitly prompted to do so."

Example User Prompt (Illustrating application of script):

"Analyze the current state of CRISPR-Cas9 gene editing technology for treating sickle cell disease. Provide a detailed overview covering:

1. The specific genetic targets involved (e.g., BCL11A enhancer).

2. Key clinical trials (phases, patient numbers, notable results, institutions).

3. Challenges and limitations (e.g., off-target effects, delivery mechanisms, ethical considerations, long-term safety).

4. Future prospects and emerging advancements (e.g., base editing, prime editing, in vivo applications). Present the information as a structured report with distinct sections and sub-sections. For each factual claim, indicate its certainty level (e.g., 'established principle,' 'emerging research suggests,' 'hypothetical application'). Ensure the report is suitable for a scientific review publication."

API System Message (if using OpenAI API):

JSON

{ "role": "system", "content": "You are an expert biomedical researcher specializing in oncology and drug discovery. Your task is to provide highly reliable, evidence-based, and detailed scientific information for academic publications and clinical decision support. Prioritize factual accuracy, comprehensive explanations, and structured output. For complex analytical questions, break down reasoning into explicit, sequential steps. Acknowledge uncertainty explicitly. Maintain an objective, formal, and authoritative tone. Do not fabricate information or citations." }

API Parameters (Example for this scenario):

• model: "gpt-4o" (or the latest high-performance model available, as these generally offer superior capabilities for complex, factual tasks)

• temperature: 0.3 (A conservative setting to balance factual accuracy with the potential for more calibrated confidence, drawing from observations that slightly higher temperatures can sometimes yield better calibrated accuracy in complex factual tasks than a strict zero, while avoiding the overconfidence associated with higher values)

• max_tokens: 2000 (Sufficient for a detailed response, providing ample space for comprehensive coverage without an excessive hard cutoff)

• stop: (To ensure structured termination of the generated report, indicating the model should stop when these sequences are generated)

Guidelines for Script Adaptation and Continuous Improvement

The provided example script serves as a robust starting point, but its effectiveness is maximized through continuous adaptation and iterative testing. Users should test the script with diverse queries relevant to their domain and refine it based on the observed quality of the outputs. The AI landscape is rapidly evolving, and the effectiveness of prompt engineering, especially fully automated approaches, continues to be an area of active investigation.

Users are encouraged to tailor the persona and instructions to their specific domain, whether it be legal, IT, content creation, or any other field, as different domains have unique requirements for reliability and detail. Vigilance for signs of hallucination—such as logical inconsistencies or fabricated data—remains crucial, and prompts should be adjusted accordingly to mitigate these occurrences.

Finally, while detail is often requested, it is important to balance this with conciseness. Excessive verbosity without substantive information can be counterproductive. Therefore, instructions should be refined to ensure that the model provides relevant detail, avoiding unnecessary elaboration. This iterative process of prompt optimization, coupled with the dynamic nature of LLM development, establishes a "dynamic equilibrium" for maintaining output quality. The script is not a final, immutable solution, but rather a framework for continuous improvement, emphasizing the ongoing role of the human prompt engineer in maintaining and enhancing the quality of LLM outputs.

Conclusion and Recommendations

The ability to adjust and optimize customization scripts for ChatGPT is not only possible but essential for achieving reliable and detailed answers. This report has demonstrated that such optimization hinges on a multi-layered approach, combining foundational system prompts or custom instructions with precise in-chat prompt engineering and careful API parameter tuning.

Key takeaways from this analysis include:

• Structured Prompting is Paramount: Clarity, specificity, and the strategic placement of instructions are fundamental. Techniques like Chain-of-Thought and Few-Shot prompting are powerful tools for guiding the model's internal processing, leading to more accurate and robust outputs.

• Nuanced Parameter Tuning: Parameters such as temperature require careful consideration. While a low temperature often correlates with factual accuracy, the optimal setting for true reliability, particularly in complex tasks, may involve a slightly higher, yet conservative, value that promotes better confidence calibration.

• Understanding LLM Limitations: Acknowledging the probabilistic nature of LLMs and their susceptibility to hallucinations is critical. Strategies must be in place to mitigate these inaccuracies, including explicit instructions to acknowledge uncertainty and the use of human feedback.

• The Confidence-Accuracy Disconnect: It is vital to recognize that an increase in accuracy through advanced prompting does not automatically translate to reliable confidence assessments from the model. Users must remain cautious and implement post-hoc verification where high-stakes decisions are involved.

• Iterative Optimization: Prompt engineering is a continuous process. The "better customization script" is not a static solution but a dynamic framework that requires ongoing testing, evaluation, and refinement based on observed output quality and the evolving capabilities of the LLM.

Based on these findings, the following strategic recommendations are proposed for users seeking to enhance ChatGPT's reliability and detail:

1. Adopt a Multi-faceted Customization Approach: Leverage both persistent custom instructions (or API system messages) for general behavioral guidelines and highly specific in-chat prompts for task-specific requirements.

2. Prioritize Specificity and Clarity: Always be explicit about the desired context, outcome, length, format, and style in your prompts. Use delimiters to separate instructions from context.

3. Leverage Advanced Prompting Techniques: Experiment with Chain-of-Thought for complex analytical tasks and Few-Shot prompting with concrete examples to guide the model towards desired output patterns and improve confidence calibration.

4. Embrace Iteration and Feedback: Treat prompt optimization as an ongoing, iterative process. Continuously evaluate the model's outputs against your criteria for reliability and detail, and refine your scripts accordingly. Human oversight and feedback are indispensable.

5. Be Aware of and Mitigate Hallucinations: Understand the causes of LLM hallucinations and implement prompt-based strategies (e.g., instructing the model to acknowledge uncertainty, providing explicit constraints) to reduce their occurrence. Always fact-check critical information.

The field of LLM customization and prompt engineering is rapidly advancing. As new research emerges and models continue to evolve, deeper understandings of LLM behavior and more sophisticated customization capabilities will undoubtedly become available, further enhancing the potential for reliable and detailed AI-generated content.

Nikolay Milyaev + AI :)