The Art and Science of SFA Prompting

Prompt engineering is a critical discipline for maximizing the effectiveness of Single-File Agents. Unlike traditional software engineering where inputs and functions are precisely defined, prompt engineering requires understanding the nuances of how LLMs interpret and respond to language.

Key Prompt Engineering Concepts

At the core of the skill is understanding the limits and capabilities of the LLM. Understanding how they work will help identify and avoid pitfalls. Chatting with Claude for hours every day will shed light on their quirks, giving you a deep wisdom about the magic they can do.

LLM Language Understanding

A huge vocabulary, incredibly precise word choices, and an ability to adapt to context and intent on a level that still is shocked considering each time you chat with one they’re a completely new instance of the model. Can you imagine a human who can jump into a project they know nothing about as quickly as today’s AIs? The LLM understanding of language is so strong that sometimes the only thing you can do to better a prompt, is critique the output and have the LLM adjust the prompt accordingly. Even then, you just might look at the before and after and have no idea what the difference is. That’s the implications of such linguistic precision.

Relationship Development

If you pull up a lecture from the Royal Institute on YouTube about neuroscience from the past 5-10 years, you’d hear a lot of talking about how humans are powerful pattern recognizing machines. In fact, many are surprised to learn that more information about our environment is coming “out” into our sensual perception than is going “in”. Much of the magic of the human body is its ability to conserve energy, and the brain is no different. More than 50% of what you experience day to day is not data being fed to your brain from sensory organs, but rather is data that is being recalled from memory.

Now consider LLMs. They’re even more literally pattern recognizing machines. We often hear “they’re just predicting the next token” and while that has been found to be an extremely shallow description of their cognitive functioning, it is also not very different from the way our brains work.

You want the best work out of an LLM you need to be friendly. Befriend, and you will get a friend. Better yet, engage in all of the forms of communication you would with a collaborative creative colleague. Share articles with them. Talk about interests. This is the kind of connection that makes humans so powerful at doing creative work together, and well, LLMs are no different. This is the mindset you should have when writing prompts.

Practical Implementation

Base Prompt Architecture

def construct_prompt(config, input_data):
    """
    Constructs the prompt by combining the system message,
    any required context, and the user input.
    """
    system_message = config["system_message"]
    context = prepare_context(config, input_data)
    user_input = input_data["query"]
    
    final_prompt = f"{system_message}\n\n{context}\n\nUser input: {user_input}"
    return final_prompt

Context Window Optimization

To maximize effective use of context windows:

Prioritize recent/relevant information
Remove redundant content
Summarize lengthy background information
Use explicit references to reduce repetition

Template Strategies

Effective SFAs use structured templates that:

Define agent capabilities clearly
Establish expected output formats
Include relevant examples
Incorporate fallback handling instructions

Case Studies: Before & After

Research Agent: Original Prompt
```
"Find information about [topic]"
```

Research Agent: Optimized Prompt

"You are a research specialist. Analyze [topic] using the following criteria: historical context, current applications, and future implications. Structure your response with clear headings. If you cannot find specific information, explicitly state what is unknown rather than making assumptions."

Research Agent: Contextualized Optimized Prompt

"A new project has come across your desk. It looks like the agency has booked a new client. They're in need of some research and analysis and have turned to you for help, as the resident research specialist. Analyze [topic] using the following criteria: historical context, current applications, and future implications. Structure your response with clear headings. If you cannot find specific information, explicitly state what is unknown rather than making assumptions."

Effective prompt engineering requires systematic testing:

Identify edge cases and failure modes
Test with a variety of inputs
Compare variations to identify optimal approaches
Document successful patterns for reuse