Smart tips for improving your LLM output and design: Real tips for AI engineer toolbox
but OK Tips Provided Efficient and reliable output no. With the capability and versatility of the language model, achieving high-quality results depends on What do you ask The model is more than the model itself. That’s where timely engineering is, not a theoretical exercise, but Built-in experience for everyday practicality In production environments, there are thousands of calls every day.
In this article, I want to share Five practical and timely engineering techniques I’m building stable, reliable, high-performance AI workflows almost every day. They are not only tips I have read, but also ways I test, refine and rely on at work.
Some may seem counterintuitive, some are surprisingly simple, but all of this has made a real change in my proficiency to get the results I expected from the LLM. Let’s dive.
Tip 1 – Ask LLM to write your own tips
The first technique may feel Counter-intuitivebut this is what I’ve been using. I usually try to make perfect tips from the beginning, but start with a rough outline of what I want and ask for LLM Tips for perfecting your ideals itselfbased on other contexts I provide. this Co-building strategies Allows rapid production very Accurate and effective tips.
The overall process usually consists of three steps:
- Start with general structure explanation of tasks and rules
- Iterative evaluation/improvement of prompts that match the desired result
- Iterative integration of edge cases or specific requirements
After LLM asked for a prompt, I ran it a few Typical examples. If the result is closed, I will not only manually adjust the prompt. Instead, I asked LLM to do this, specifically General calibrationbecause LLM tends to patch things in too specific ways. Once I get the required answers for more than 90% of the cases, I usually run it Batch input data Analyze edge cases that need to be solved. I then submit the question to the LLM, explaining the question when submitting the input and OUPUT to iterate over the adjustment prompts and get the desired result.
A good tip that usually helps is Ask LLM to ask questions Before making timely modifications to ensure they are fully aware of the requirements.
So, why is it so good?
one. It is structured immediately better.
Especially for complex tasks, LLM helps structure problem space in both ways logic and operate. It can help me too Clarify my own thoughts. I avoid getting stuck in the grammar and focus on solving the problem itself.
b. It reduces contradictions.
Since LLM converts the task into “its own words”, it is more likely to detect ambiguity or contradiction. When doing so, it often asks for clarification before bringing up a clean, conflict-free formula. After all, who compares the news The person who was going to explain it?
Think of it as communicating with people: a large part of poor communication comes from different explanations. LLM sometimes finds something that I think is obvious to be unclear or contradictory… In the end, this is the person who does the job, so this is It is Important explanation, not mine.
c. It can be summarized better.
Sometimes it’s hard for me to find clear, abstract expressions for the task. LLM is good at this. It discovered the pattern and produced a generalized hint that is more scalable and powerful and can make me generate it myself.
Tip 2 – Use Self-Assessment
The idea is simple, but again very powerful. The goal is Force LLM to self-evaluate the quality of its answers Before output. More specifically, I ask Rate your own answer For example, on a predefined scale, from 1 to 10. If the score is below a certain threshold (usually I set it to 9), I ask it Try again or promote The answer depends on the task. I sometimes add the concept of “if you can do better” to avoid endless loops.
In practice, I find LLM’s tendency to be similar to humans fascinating: it’s usually for The easiest answer, not the best answer one. After all, LLM is trained with human-produced data and is therefore designed to replicate the answer pattern. So, give it a Clear quality standards Helps significantly improve final output results.
Similar methods can be used Final quality inspection Focus on rules and regulations. The idea is to ask LLM to review its answers and confirm whether it complies with specific rules or all rules before sending the response. This can help improve the quality of the answer, especially when a rule sometimes skips. However, in my experience, this approach is more effective than requiring self-distribution quality scores. When this is needed, it can mean that your tips or your AI workflow needs improvement.
Tip 3 – Use a responsive structure plus targeted examples to combine format and content
Using examples is a well-known and powerful way to improve results. As long as you don’t have an exaggeration. A carefully selected example is often more helpful than many teaching practices.
On the other hand, the responsive structure helps to define exactly the appearance of the output, especially for technical or repetitive tasks. It avoids surprises and keeps the results consistent.
The example then complements the structure by showing how to fill it with the processed content. This «Structure+Example» combination tends to work well.
However, the examples are usually Heavy texttoo much can be used The most important rule for dilution or lead to their tracking level being less consistent. They also increase the number of tokens, which can lead to side effects.
So, using examples wisely: One or two carefully selected examples covering most of your basic or edge rules are usually enough. Adding more may not be worth it. It can also help add A brief explanation After the example, it is reasonable to prove that the reason it matches the request, especially if this is not very obvious. I personally rarely use negative examples.
I usually give one or two positive examples and the general structure of the expected output. Most of the time I choose XML tags
Giving examples is especially useful when structures are nested. It makes things clearer.
## Here is an example
Expected Output :
-
My sub sub item 1 text
My sub sub item 2 text
My sub item 2 text
My sub item 3 text
-
My sub item 1 text
My sub sub item 1 text
Explanation :
Text of the explanationTip 4 – Break down complex tasks into simple steps
This seems obvious, but it is crucial to keep the quality of the answers high when dealing with complex tasks. This idea is Divide a major task into several smaller, well-defined steps.
Just like the human brain struggles when it has to multitask, LLM tends to produce lower-quality answers when the task is too broad or involves too many different goals at once. For example, if I ask you to calculate 125 + 47, then 256 – 24, last one after another, one after another, that should be fine (hopefully :)). But if I could give me three answers at a glance, the task would become more complicated. I like to think LLM behaves the same.
So instead of doing everything like I’d proofread the article, translate and format it in html, I’d break the process into two or three simple steps, each handled by a separate prompt.
The main drawback of this approach is that it adds some complexity to your code, especially when passing information from one step to another. But modern frameworks like LangchainI personally like and use whenever I have to deal with this situation, making this order of task management very easy to implement.
Tip 5 – Ask LLM for explanation
Sometimes, it’s hard to understand Why LLM gave an unexpected answer. You may start guessing, but the simplest and most reliable way may be simply Ask the model to explain its reasoning.
Some might say that the predictiveness of LLM does not allow LLM to actually interpret its reasoning, because it just does no Reason, but my experience shows:
1- Most of the time, it will be effectively outlined Logical explanation This produces its response
2- Modifications in time according to this explanation usually correct the wrong LLM answer.
Of course, this is not proof that LLM is actually reasoning, and Prove that this is not my jobbut I can point out that the solution is very effective in Pratice to optimize quickly.
The technology is particularly useful in development, pre-production, and even in the first few weeks after it goes live. In many cases, it is difficult to predict all possible edge cases while relying on one or several LLM calls. Be able to understand Why This model produces certain answers that can help you design the most precise fix that solves the problem without causing unnecessary side effects elsewhere.
in conclusion
Working with LLMS is a bit like working with a Genius intern who is crazy fast and capable, but usually goes in a mess and heading in every direction if you don’t clearly state what you expect. Getting the most out of the intern requires clear instructions and some management experience. The same is true for llms Smart tips and experiences make everything different.
The five techniques I’ve shared above are not “magic”, Practical Methods I go every day Out of general results Get it using standard tips and get the high quality technology I need. They have always helped me turn the correct output into excellent output. Whether it’s co-designing tips with the model, breaking the task into manageable parts, or simply asking LLM Why What is the response? These strategies have become an important tool for my daily work to create the best AI workflow I can.
Timely engineering is more than just writing clear and well-organized instructions. It’s about understanding how Model explanation They and design your approach accordingly. Timely engineering is to some extent an art, a nuance, skill and personal style, and no two timely designers write the same lines, resulting in different results in Strenght and weaknesses. After all, llms lasts one thing: The better you talk to them, the better they work for you.
