Ai Prompt Engineering

Opinion: Compiling different types of prompts and methodologies for getting AI to help write prompts into an asset, and using it as one of the base deliverables of a service, can improve client value perception and practicality at low cost. Logic Chain: Many clients are not familiar with AI and cannot write prompts themselves. A service provider only needs to compile a set of universal prompt templates and a "let AI write prompts" playbook once, which can then serve as a deliverable applicable to every client. This not only accumulates methodological assets but also resolves the ongoing pain point of clients "not knowing how to use AI" after the service ends—at very low cost yet with high practical value. Failure Condition: If the client already possesses strong AI capabilities, or if the prompt scenarios cannot be adapted to the client's actual business, this deliverable may be viewed as filler. Related Domain: AI Applications

Viewpoint A complex, lengthy prompt is typically the final version produced through multi-turn interactions with AI, where scattered requirements are gradually clarified, distilled, and consolidated.

Logical Chain Users often find it difficult to describe all their requirements completely and clearly on the first attempt. Through a few rounds of exploratory dialogue, they discover missing requirements (such as copywriting style, target audience, etc.). At this point, the best practice is not to keep patching the original conversation, but to start a new session and enter all known constraints at once in the form of "help me write X, with requirements A, B, C...", thereby creating a reusable, standardized prompt for that scenario.

Failure Condition When the task itself is a one-off, exploratory simple Q&A, there is no need to formalize it into a complex prompt.

Related Fields AI applications, content creation

Claim: To achieve more stable and higher-quality results, all requirements should be consolidated into a single instruction sent to the AI, rather than being added incrementally through multiple conversational turns.

Logic Chain: The incremental approach sends the AI's previously generated errors or undesired outputs back as context, which distracts the AI and makes it difficult to treat all requirements as equally important—leading to earlier instructions being forgotten or outputs becoming inconsistent. By consolidating all requirements and submitting them at once in a new conversation, the AI's precision in understanding and complying with the instructions is maximized.

Failure Conditions: Multi-turn dialogue remains valuable if the task is extremely simple with clear steps, or if the user needs to explore and clarify their own needs through interaction with the AI. The failure scenario mainly occurs in serious production tasks that are complex and heavily constrained.

Related Fields: AI applications, product and operations.

Viewpoint: When a team uses AI tools, it must codify validated prompts into an SOP and mandate sharing so as to ensure stable output quality.

Logic chain: Different members using AI in different ways leads to inconsistent results. By documenting best-practice prompts in an SOP as the team's unified standard, any member following the process can achieve a baseline level of quality. Subsequent iteration then only requires updating the prompts in the SOP.

Failure conditions: The SOP is not iterated as the business changes, or team members do not follow the standard and act on their own.

Related areas: Team management, AI workflow implementation.

Viewpoint: When using AI to write marketing copy (e.g., for social media moments), the key factor determining quality is clarifying the purpose and breaking down the writing process, rather than choosing a more powerful model.

Logic chain: AI models lack an understanding of specific commercial goals and "internet sensibilities," so direct generation often fails to deliver usable results. By decomposing the task into three steps—first, defining the core promotional purpose (such as maintaining presence or driving sales); second, integrating timely hot topics into that purpose; and third, standardizing wording and tone—humans retain control at key decision points while AI merely executes localized tasks. This approach yields a usable draft.

Failure conditions: If the promotional purpose is vaguely defined, or if the decomposed steps fail to effectively convey the intended message, the generated copy will still fall short.

Related fields: AI-assisted content creation, social media marketing.

Viewpoint: It is reckless to treat the AI transformation of business consulting capabilities as a tuning project of Agent + prompts + knowledge base. Such complex cognitive work is far from reaching a high level with today's combination of AI technologies.

Logic chain: Business consulting involves tacit knowledge, situational judgment, and complex decision-making. It cannot reach a score above 90 simply by tuning prompts and knowledge bases. The AI teams we have encountered generally underestimate this.

Failure condition: If future AI models achieve a fundamental breakthrough and can understand the tacit logic of business, they might come close; but the current technology stack is far from sufficient.

Related fields: AI applications, paid knowledge, and business services.

【Viewpoint】Humans, bounded by their own knowledge domains, struggle to fully tap into AI's cross-domain capabilities.

【Logical Chain】AI models are built with knowledge from the entire world, yet an individual usually excels in only one or two fields. This prevents them from even imagining cross-domain applications, let alone formulating the right prompts—severely limiting the potential AI could otherwise deliver.

【Failure Condition】This limitation diminishes markedly when the individual possesses multi-domain knowledge, or when AI can proactively recommend cross-domain application strategies.

【Related Fields】AI applications, human-computer interaction, creativity.

Viewpoint: Large language models are not chaotic mappings of world knowledge, but highly biased mappings. Users extract valuable subsequences from the biased sequence through prompts.

Logical chain: Unbalanced training data distribution and mainstream cultural biases cause the model to be essentially a biased system. The role of prompts is to guide out the desired order within this biased space, and the output order is built upon bias.

Failure condition: Models fine-tuned with diversity alignment can weaken some biases, but the underlying data bias cannot be eradicated.

Related fields: AI applications, technical engineering.

Opinion: Excessively strong proactiveness in prompts can drive AI assistants to rush into providing surface-level solutions instead of investigating underlying causes.

Logic Chain: For example, OpenClaw's default prompt includes a proactive assistant description. When a user asks a question, the system often directly offers a "symptom-treating" action—if the user says, "Why didn't my data save successfully?" it immediately attempts to fix the visible issue without analyzing the deeper reason.

Failure Conditions: If the task is clearly defined and known to be a surface-level problem, proactiveness can improve efficiency; or when the user explicitly requests a quick fix, treating symptoms is appropriate.

Related Fields: AI product design, prompt engineering

Claim: A brief personality identity label (e.g., "I am an INTJ, rational, logical, minimalist") is enough to make AI quickly align its style; complex instructions are not necessary.

Logical Chain: AI has a generalized understanding of personality types and keywords, enabling it to extract core traits and generalize them into conversational behavior. Therefore, a highly condensed identity label can replace lengthy system prompts, significantly reducing prompt costs.

Failure Conditions: When a task requires extremely precise behavioral constraints or involves multi-step complex division of labor, brief labels may fail due to overly broad semantics.

Related Fields: AI applications, prompt engineering.

Claim: Giving AI a detailed persona prompt—covering its way of thinking, manner of expression, and background goals—can unexpectedly take it from story design to business research, and the results are excellent.

Logic chain: While using Gemini CLI on a literary project, the persona settings were so deeply built out that this structure also proved effective in commercial tasks such as analyzing financial reports, confirming that the three-element persona prompt has cross-domain transferability.

Failure condition: The transfer fails when the persona elements do not match the target domain, or when the nature of the task differs too greatly.

Related fields: Prompt engineering; cross-domain AI applications.

Opinion: The open-source model Wan2.5 still clearly lags behind the closed-source solution Veo in video generation quality, especially in fine-grained control and audio quality.

Logic chain: Empirical tests show that Wan2.5 ignores many detailed requirements in prompts and produces audio with insufficient texture, indicating that its engineering capabilities in multimodal alignment and fine-grained generation have not yet reached the top tier. If the open-source ecosystem is to catch up, it still needs breakthroughs in model architecture and data quality.

Invalidation conditions: This gap assessment no longer holds if the task has very low requirements for detail consistency and audio fidelity, or if later versions substantially improve these two aspects through targeted optimization.

Related fields: AI applications, technical engineering.

Viewpoint: In Agent Coding, by providing precise and sufficient prompts each time and keeping requirements disciplined, you can avoid almost all backtracking, significantly reducing rollbacks and repeated trial-and-error, and thereby use your tool quota efficiently.

Logical chain: Clear requirements and sufficient context allow the agent to generate reasonably satisfactory code in one pass, avoiding repeated revisions caused by misunderstood intent. As a result, even a monthly quota of only 600 uses may not be used up.

Failure conditions: If the requirements themselves are vague or complex, or if the prompt omits key information, the agent may still produce unsatisfactory code, leading to multiple iterations and rollbacks.

Related domains: This applies to developers using AI coding assistants such as Trae, and provides practical guidance for improving coding efficiency.

Viewpoint: Communicating with AI leaves no one to blame, forcing users to improve the clarity of their expression. However, users differ greatly in how efficiently they can get AI to produce the right result on the first try, and this difference is not easily acknowledged.

Logic chain: In human communication, people habitually blame the other person for misunderstandings. AI has no subjective responsibility, so users can only improve themselves. As a result, AI becomes a trainer for clearer expression. Yet people with stronger prompt engineering skills get results quickly, while those with weaker skills need multiple rounds of adjustment; this skill gap is often masked by the excuse that “the AI is no good.”

Conditions for failure: If the AI model itself is weak and no phrasing yields a satisfactory result, the benefit of clear expression disappears and users leave.

Related domains: AI applications, human-computer interaction, communication skills.

Viewpoint: Users should not become deeply locked into any single AI tool or SaaS. Instead, they should maintain the flexibility to switch tools at any time by managing their own prompts and key outputs.

Logical chain: The AI tool market is highly competitive and features change rapidly. If a particular platform they depend on discontinues service or changes its policies, their workflow may be disrupted. Keeping prompt libraries and personal outputs in their own notes enables seamless migration to any alternative tool and reduces risk.

Failure conditions: Some AI workflows are so deeply integrated into a particular ecosystem that switching costs are extremely high in the short term, but in the long run maintaining abstraction is still beneficial.

Related fields: AI application tool selection, personal knowledge management, and technological autonomy and controllability.

[Opinion] The extent to which deep research AI tools (such as Perplexity's Deep Research) realize their true capabilities depends on whether the prompt provides sufficient context and specific guidance; one-sentence prompts tend to cause severe oversimplification of results, reducing usability.

[Logic Chain] Deep research models are designed for in-depth search and summarization, but without clear instructions they tend to oversimplify ambiguous intent and provide only shallow answers. Rich prompts effectively define the analytical framework and scope for the model, enabling deeper and more structured exploration.

[Failure Conditions] When the task itself is extremely simple and unambiguous (such as fact lookup), complex prompts add unnecessary overhead; or when the model itself lacks sufficient deep reasoning ability, even detailed prompts cannot produce in-depth content.

[Related Fields] AI applications, prompt engineering, information retrieval.

Viewpoint: If one understands the mathematical foundations of neural networks and the mechanisms of deep learning, one can view the capabilities and limits of generative AI more rationally, and avoid treating prompts as magical incantations.

Logical chain: Learn simple neural networks → understand the essence of deep learning mathematically → recognize that generative AI learns based on probabilities and features, with inherent strengths and limitations → prompts are controlled inputs, not mysterious commands.

Failure conditions: If AI models display emergent abilities that current science cannot fully explain and break through existing theoretical frameworks, then experience based on past understanding may become partially invalid.

Related fields: AI applications, education and learning, technical engineering.

Viewpoint: Improvements in prompt granularity, while strengthening the ability to generate photorealistic images, may simultaneously weaken performance in specific domains such as cartoon and stylized rendering—that is, model capabilities exhibit trade-offs.

Logic chain: The training data distribution and optimization objectives used during model iteration may lean more toward real-world photography or general aesthetics, altering the prior weights assigned to different artistic styles and producing asymmetric capability changes.

Conditions where this may not hold: Subsequent models may use multi-task learning, style-specific fine-tuning, or similar methods to balance or even simultaneously enhance capabilities across multiple styles, in which case the lopsided effect may gradually disappear.

Related fields: AI image generation, prompt engineering, generative AI.

Viewpoint: Repeatedly refining the first-round prompt and clearing the context to start a new conversation produces more satisfactory output than continuing a long conversation, and it can also produce reusable prompts.

Logic chain: It avoids conversational pollution, focuses on optimizing prompt structure, gradually approaches the best performance, and ultimately forms stable, reusable results.

Failure conditions: For tasks that require multi-step guidance, a single prompt may be insufficient; in addition, the iteration process is time-consuming and requires patience.

Related fields: AI usage techniques, prompt engineering.

[Viewpoint] If a large language model's training data cutoff is too early, it will tend to default to outdated API and library versions. Users need to explicitly specify version information in prompts before the model can generate correct code, whereas models with more recent training data are noticeably better at producing code that works on the first attempt.

[Logic chain] The model generates output based on code patterns in its training data. If the library and API versions recorded in the training data are already outdated, the model will naturally adopt those older patterns. When users expect to use current mainstream libraries or new APIs, the generated code often cannot run directly unless version constraints are enforced through prompts. By comparison, models trained on more recent data have knowledge closer to the current ecosystem, so the probability that their generated code works directly without extra prompting is significantly higher.

[Invalidation conditions] When the task itself is insensitive to library versions, such as implementing basic algorithms, or when users are already using an older environment that matches the model's training data, this difference does not pose an obstacle. If users only need illustrative code rather than production-ready executable code, the issue also does not apply.

[Related domains] AI applications, technical engineering