This content originally appeared on DEV Community and was authored by cursedknowledge
Prompt engineering has transformed in recent years from a set of intuitive “life hacks” into a full-fledged scientific discipline at the intersection of psychology, linguistics, and computer science. Working with language models today requires not just “asking the right questions,” but a deep understanding of the principles of their functioning and a systematic approach to formulating problems.
In this article, we will consider scientifically based methods that are qualitatively different from typical recommendations like “be specific” and “use simple language.” We will focus on approaches confirmed by research and analyze how they affect the quality of the results obtained.
Metaprompting: When a model refines itself
Metaprompting is a technique where an initial query generates a more detailed subquery, allowing the model to “re-question itself” to refine the task. Research suggests that metaprompting allows the model to activate hidden knowledge and approaches, which is especially effective for complex tasks. The model is able to refine details and build its own chain of reasoning.
Instead of:
“Write an article about the impact of social media on teenagers.”
Use:
“Act as a social psychology researcher. I want to write an article about the impact of social media on teenagers. What are the key aspects that I should consider? After you list the aspects, formulate for yourself a complete technical task for writing such an article, and then write the article itself according to this technical task.”
In this case, the model will first create the structure of the study, then formulate a detailed technical task, and only then generate the final text. This approach reduces the likelihood of missing important aspects and increases the systematicity of the analysis.
Chain-of-Thought (CoT): the art of step-by-step reasoning
Chain-of-Thought is a technique that encourages the model to explicitly demonstrate the reasoning process, which improves accuracy on logical and mathematical problems.
Research shows that adding the phrase “Let’s think about it step by step” improves the accuracy of LLM on logical and mathematical problems by 20-40%. Subsequent work has shown that a structured approach to reasoning reduces the likelihood of “hallucinations” and improves the validity of inferences.
Advanced Applications
The basic CoT familiar to many users can be improved by using the self-consistency technique. Self-Consistency is a series of duplicated chains of reasoning that converge at a single point, symbolizing comparison and selection of the best answer.
Solve a delivery route optimization problem for 5 points with coordinates: A(0,0), B(5,5), C(3,7), D(8,2), E(2,9).
- Calculate the distances between all points
- Plot 3 different route options using different algorithms
- For each option, estimate the total distance and time
- Compare the results and choose the optimal route
- Check if your solution has any logical errors
- Explain why the chosen route is optimal
This approach doesn’t just break the problem down into steps, but forces the model to generate multiple solutions and then choose the most consistent one — a method that was shown to improve accuracy by 15% in a study.
Role context: activating expert knowledge
Assigning a model a specific role allows you to activate the corresponding patterns in the training data and customize the response style.
Research shows that assigning a professional role increases the depth and accuracy of answers by 30%. This is because the model begins to use specific knowledge and terminology related to this area.
Instead of simply indicating the role (“You are a programmer”), use a detailed professional context:
You are a leading software architect with 15 years of experience working on high-load systems. Your design approach is known for its emphasis on performance and scalability.
Design a backend service architecture to process 10,000 transactions per second with mandatory support for horizontal scaling. The system must remain operational if any of the components fail.
Such a detailed description of the role directs the model not only to use professional terminology, but also to apply a certain approach to solving the problem, which makes the answer more holistic and consistent.
Structured formatting: control over the output
Explicitly specifying the format of the response is one of the most powerful tools of prompt engineering, allowing you to control not only the content, but also the organization of information.
Using structured formats (JSON, markdown, tables) significantly reduces the number of “hallucinations” and increases the informativeness of responses. This is due to the fact that the model is forced to follow clear rules for data presentation.
Analyze 3 frameworks for front-end development: React, Vue and Angular. Present the analysis in the following format:
{
"frameworks": [
{
"name": "Framework Name",
"strengths": ["Strength 1", "Strength 2", ...],
"weaknesses": ["Weakness 1", "Weakness 2", ...],
"learning_curve": "score from 1 to 10",
"best_use_cases": ["Use Case 1", "Use Case 2", ...],
"community_metrics": {
"github_stars": number,
"npm_downloads_monthly": number,
"active_contributors": number
},
"performance_score": "score from 1 to 10 with justification"
},
...
],
"comparison_summary": "Comparison text with argumentation",
"recommendation_by_project_type": {
"enterprise": "framework name with reasoning",
"startup": "framework name with reasoning",
"personal_project": "framework name with reasoning"
}
}
All estimates must be based on up-to-date data.
Adaptive Iterative Refinement: A Dialogue Instead of a Monologue
Adaptive iterative refinement is an approach where a query is gradually refined based on previous model responses.
Scientific Justification
The study proposed a “self-refinement” method, where the model successively refines the query by asking itself questions. This method has been shown to reduce the number of errors by 25% in complex analytical tasks.
Step 1: “List the main problems of scaling a microservice architecture.”
Step 2: “From the listed problems, select 3 most critical for fintech projects. Explain why they are the most important in this context.”
Step 3: “For the problem [specific problem from the answer to Step 2], propose 3 architectural patterns that help solve it. Evaluate each pattern based on the following criteria: implementation complexity, efficiency, infrastructure requirements.”
This approach allows you to gradually narrow the focus of the study and get more specific and in-depth answers, avoiding superficial analysis.
Conclusion: Prompt Engineering as a Scientific Discipline
Prompt engineering has evolved from intuitive “tricks” to a scientifically sound discipline based on serious research. The key to using language models effectively is to combine different techniques:
- Use metaprompting for complex problems
- Use CoT for logical and mathematical problems
- Use role context to activate specific knowledge
- Control the structure of the output with clear formats
- Use iterative refinement for deep analysis
Instead of blindly following template recommendations, it is worth experimenting with different approaches and analyzing their effectiveness for specific problems. Prompt engineering is not a set of universal recipes, but an exploratory process that requires an understanding of the principles of models and a systematic approach to formulating queries.
The future of this field lies in automating prompt creation via RLHF (Reinforcement Learning from Human Feedback) and developing tools for objectively assessing the quality of prompts. But even now, based on existing research, it is possible to significantly improve the efficiency of interaction with language models by avoiding obvious solutions and using scientifically proven methods.
More interesting information on the topic of prompting can be found in my Telegram channel.
This content originally appeared on DEV Community and was authored by cursedknowledge