X | @rali2100 - Linkedin|R Ali
Created: 2025-10-20
In the contemporary digital landscape, Artificial Intelligence (AI), particularly Large Language Models (LLMs), has transitioned from a theoretical novelty to a practical tool for content generation. However, a significant challenge persists for expert users: eliciting truly comprehensive, in-depth, and unrestricted long-form content. By default, many models exhibit a 'brevity bias', favouring concise summaries over exhaustive explorations. This disposition stems from their training data and the very reinforcement learning paradigms designed to make them 'helpful'.
This article presents a critical analysis of the strategies required to transcend this default behaviour. It moves beyond simple commands to explore the technical and conceptual frameworks of "prompt engineering" necessary to mandate thoroughness. We will evaluate the failure points of naive prompting, introduce sophisticated methodologies for ensuring depth, and provide practical case examples. The objective is to equip the writer, researcher, or analyst with the tools to transform the AI from a simple summariser into a genuine partner in long-form, analytical writing.
Before one can successfully command an AI to write at length, one must understand why it defaults to brevity. This tendency is not a sign of laziness but a direct consequence of its design and optimisation.
First, the training data for many foundational models is vast and varied, but it includes a high volume of question-and-answer formats (e.g., 'What is X?'), dialogue, and summarisation tasks. The model learns that a successful interaction is often one that provides a direct, efficient answer.
Second, the most significant factor is the refinement process known as Reinforcement Learning from Human Feedback (RLHF). During this phase, AI-generated responses are ranked by human reviewers. These reviewers, often working to efficiency metrics, tend to reward answers that are correct, clear, and, crucially, concise. An AI that provides a 5,000-word treatise on a simple query would likely be down-ranked for being 'unhelpful' or 'verbose'. This process systematically trains the model that 'good' output is often 'short' output.
Finally, the technical constraint of the context window—the finite amount of text (both input and output) the model can process at one time—plays a psychological role. The model is optimised to perform tasks well within this limit, reinforcing a operational pattern that avoids testing its boundaries.
The result is a 'brevity bias': a default operational mode that prioritises summarisation and rapid problem-resolution over deep, exploratory analysis. Simply stating "write a long article" is often insufficient to overcome this deeply ingrained training.
A common but flawed approach is the use of simple negation or subjective descriptors. Prompts such as "Do not write a short article" or "Write a very long piece" are destined for failure.
This strategy is critically flawed because it is imprecise. 'Long' is a subjective measure. For an AI trained on short-form text, a 1,000-word article may be considered 'long', whereas the user may have been expecting 5,000 words. The AI lacks the user's specific context and intent.
When faced with such a vague mandate, the model will often resort to "padding". It may extend the article by:
Repetition: Stating the same core idea in multiple ways.
Superficial Detail: Adding low-value, generic information that increases word count but not analytical depth.
Circular Structure: Ending the article close to where it began, without a strong analytical progression.
The prompt fails because it instructs the AI on what not to do (be short) without providing a clear, affirmative instruction on what to do (be comprehensive, analytical, and structured). The key is not to negate brevity, but to mandate thoroughness.
To secure truly in-depth output, the user must shift their role from one of a simple questioner to that of a project director. This involves providing clear, structural, and goal-oriented instructions.
1. The Comprehensive Mandate: Defining the Task's Goal
The most effective strategy is to replace subjective adjectives like "long" with specific, professional descriptors that define the purpose of the content.
Instead of "write a long article," one should use a "comprehensive mandate." This involves using a lexicon of specialist terms that imply depth and completeness.
Consider the following linguistic shifts:
Instead of "long," use "comprehensive," "exhaustive," "in-depth," or "detailed."
Instead of "article," specify the format: "a detailed analysis," "an exhaustive guide," "a white paper," or "a thorough exploration."
Explicitly state the priority: "Prioritise thoroughness and complete coverage over brevity."
Explicitly remove the constraint: "There is no word count limit; write as much as is necessary to explore the subject fully."
This reframes the task. The AI is no longer trying to meet a vague length requirement; it is trying to fulfil a professional standard of "comprehensiveness."
2. Structural Scaffolding: Providing the Skeleton
The second core strategy is to relieve the AI of the burden of both structuring the content and writing it. By providing a structural scaffold, the user dictates the required points of analysis, forcing the AI to dedicate text to each one.
This can be done by including a clear outline or list of required topics within the prompt itself.
Weak Prompt: "Write a detailed article about renewable energy."
Strong Prompt: "Write a comprehensive analysis of renewable energy. You must ensure all relevant aspects are discussed in detail, dedicating a specific, in-depth section to each of the following: (1) The economic viability of solar power vs. wind power; (2) The logistical challenges of battery storage and grid integration; (3) The geopolitical impact of reducing reliance on fossil fuels; and (4) The role of public policy and subsidies in market adoption."
In the second example, the AI cannot write a "short" article, because to do so would mean failing to address the four specific, complex sub-topics mandated by the prompt.
3. Iterative Generation and Chaining
For tasks of extreme length (e.g., a book chapter, a technical manual), one must recognise the technical limitations of the context window. No single prompt, however well-crafted, may be able to generate 20,000 words in one response.
The solution is iterative generation, also known as "chain-prompting." This technique turns the writing process into a dialogue.
Prompt 1 (The Outline): "Generate a comprehensive, multi-level outline for an exhaustive guide on [Your Topic]. The guide should have at least five major sections, each with multiple sub-points."
Prompt 2 (The First Section): "Excellent. Now, using that outline, write only the Introduction and Section 1 ('[Title of Section 1]'). Ensure you explore all sub-points from the outline in detail. Do not write Section 2 yet."
Prompt 3 (Continuation): "Thank you. Please proceed by writing Section 2 ('[Title of Section 2]') in the same comprehensive manner."
Prompt 4 (The 'Continue' Command): If the AI stops mid-generation (often due to output token limits), a simple prompt of "Please continue" or "Proceed with the next paragraph" will usually force it to resume from where it left off.
This method is the most robust. It provides maximal user control, bypasses technical limits, and allows for course correction at each stage.
Let us analyse three scenarios to illustrate the practical difference between these prompting methods. These examples are presented as prose analyses, as requested.
Case Example 1: The Failed "Vague Mandate"
Prompt: "Write me a long article about the history of the Japanese Samurai."
Analysis of Result: The AI will likely produce a standard, 1,000 to 1,500-word encyclopaedia-style summary. It will correctly identify the start (Heian period), the peak (Edo period), and the end (Meiji Restoration). It will mention key concepts like bushido, daimyo, and shogun. However, the article will feel superficial. It fulfils the "long" command relative to a simple definition, but it lacks any real analytical depth. It is a summary, not an analysis. The prompt failed because "long" is a subjective and low-effort instruction.
Case Example 2: The Successful "Comprehensive Mandate"
Prompt: "I am writing a piece for an historical journal. Generate a comprehensive analysis of the decline of the Samurai class during the Meiji Restoration. Prioritise thoroughness over brevity; there is no length restriction. You must ensure your analysis explores, in detail: (1) The political and social motivations for the dissolution of the class; (2) The specific economic impact of the Haitōrei Edict (sword ban); (3) The Samurai's role in the Satsuma Rebellion; and (4) The philosophical crisis of bushido in a modernising Japan."
Analysis of Result: The output is transformed. The AI is no longer writing a simple "history of." It has been tasked with a specific, multi-faceted analysis. The prompt forces the AI to generate significant, detailed text for each of the four required components. The resulting article will be substantially longer, but more importantly, it will be analytically dense and well-structured. The AI's 'brevity bias' is successfully overridden by a set of specific, complex tasks that cannot be fulfilled with a simple summary.
Case Example 3: The "Iterative Generation" for Maximal Length
Prompt 1: "Generate an exhaustive, chapter-by-chapter outline for a complete beginner's guide to the C++ programming language. The outline must cover everything from basic syntax to object-oriented programming and the Standard Template Library (STL)."
Analysis of Result 1: The AI produces a detailed, multi-level outline, perhaps 10-15 main chapters with 5-10 sub-points each.
Prompt 2: "Using the outline, write Chapter 1: 'Introduction to C++ and Basic Syntax'. Ensure you explain variables, data types, and operators with clear code examples and detailed explanations for each."
Analysis of Result 2: The AI generates a long, detailed chapter focused only on that first topic. It will include code blocks, explanations, and context, as requested.
Prompt 3: "Please continue with Chapter 2: 'Control Structures (Loops and Conditionals)'."
Analysis of Result 3: This process is repeated for all 15 chapters. The user is, in effect, co-writing a book. This method is the only reliable way to produce content that may run to hundreds of pages, completely circumventing any single-prompt generation limits and ensuring total comprehensiveness.
Beyond these core strategies, advanced users can employ creative techniques to further enhance depth.
Persona Crafting: Instructing the AI to adopt an expert persona attunes its output to a higher standard of detail. For example: "Act as a professor of material science" or "Act as a regulatory compliance lawyer." This prompt implicitly signals that a superficial answer is unacceptable; the response must reflect the assumed expertise, which naturally leads to more detailed and specialised language.
Conceptual Scaffolding: This involves asking the AI to "think" before it "writes." A prompt might instruct: "Before you write the article, please state the central thesis you will defend. Then, list the key arguments you will use to support it." This forces the AI to establish a logical framework, which leads to a more coherent and well-supported long-form argument, rather than a simple collection of facts.
To ensure clarity, the following is a detailed explanation of the specialist terms and core ideas used throughout this article.
Brevity Bias:
Explanation: This is the default tendency of many LLMs to provide concise, summary-level answers rather than detailed, long-form explanations. It is an emergent behaviour resulting from training data (which includes many short Q&A pairs) and the RLHF process, which often rewards efficient, short answers.
Comprehensive Mandate:
Explanation: This is an advanced prompting technique. Instead of using vague subjective terms like "long," the user gives a formal, professional command using a specific lexicon (e.g., "comprehensive analysis," "exhaustive guide," "in-depth exploration") that signals a requirement for professional-grade thoroughness, not just increased word count.
Context Window:
Explanation: This is the fixed technical limit on the amount of text (measured in 'tokens', which are pieces of words) that an AI model can 'remember' or process at one time. This limit applies to both the user's input (the prompt) and the AI's output (the generation). It is the primary technical reason why an AI cannot generate a 100,000-word novel in a single response.
Iterative Generation (or Chain-Prompting):
Explanation: This is the process of generating a very long piece of content by breaking it down into a series of smaller prompts. Typically, the user first asks for an outline, and then asks the AI to write the article section by section. This dialogue-based method bypasses context window limitations and allows for greater user control.
Large Language Models (LLMs):
Explanation: This is the specialist term for the type of AI discussed. An LLM is a complex neural network trained on vast quantities of text data, enabling it to understand, generate, and process human language.
Persona Crafting:
Explanation: An innovative prompting technique where the user instructs the AI to "act as" a specific expert (e.g., "Act as a constitutional lawyer," "Act as a chief marketing officer"). This attunes the AI's vocabulary, tone, and analytical depth to that specific role, often resulting in more detailed and sophisticated content.
Prompt Engineering:
Explanation: This is the specialist term for the skill of designing and refining the input (the 'prompt') given to an AI to achieve a specific, desired output. It is the core subject of this article.
Reinforcement Learning from Human Feedback (RLHF):
Explanation: This is a crucial step in the training of modern LLMs. After initial training on raw text, the AI's responses are shown to human reviewers who rank them for quality, helpfulness, and safety. The model is then 'reinforced' (finetuned) to prefer generating responses similar to those that received high rankings. This process is a primary cause of the 'brevity bias', as concise answers are frequently ranked highly.
Simple Negation:
Explanation: This refers to the weak and ineffective prompting strategy of using negative commands like "Do not make it short." This fails because it tells the AI what not to do, rather than providing a clear, affirmative, and specific goal for what it should do.
Structural Scaffolding:
Explanation: This is the technique of providing the AI with a clear structure (like a detailed outline or a list of required topics) within the prompt itself. This acts as a 'scaffold' that forces the AI to build its content around a pre-defined framework, ensuring all required sub-topics are covered in detail and preventing a superficial summary.