4 Elements of Output in AI Solutions

1. Large Language Model (LLM)
The LLM is the foundation of the AI solution. Each model has different strengths and limitations — affecting reasoning, creativity, and factual accuracy. Models also differ in token capacity (e.g. GPT-4: 128k tokens, Claude: 200k tokens), which determines how much text they can process at once. Choosing the right model for the task matters, since some excel at creative writing while others excel at factual analysis.

2. External Sources
Attached files expand the model's knowledge and align it with a specific subject area, giving you control over what the response is based on. Common attachments include court cases, legal articles, firm templates, past work examples, and reference material. Quality and relevance matter — better sources mean more accurate output. Our solution is GDPR-safe. Always check sources for accuracy, use clean and standard file formats (the model doesn't read images, video, or audio today), and avoid overloading the model with too much material, which can dilute focus.

3. System Prompt
Often called the model's "personality," the system prompt shapes how it presents information. It can be customized for specific use cases, tones, or domains, making output more consistent and predictable. Prompts can be simple or advanced, and keeping them consistent across similar tasks leads to more reliable results. Testing different versions helps identify what works best.

4. User Prompt
The user prompt defines the task — its complexity is limited only by your imagination. Techniques include one-shot prompting (a single guiding example) and series prompting (a sequence of steps for multi-step tasks). Use frameworks like COSTAR to structure prompts, ask the model for feedback on how to improve them, and keep language clear and simple. For complex tasks, break instructions into clear steps, and refine your prompt over time based on results.

Good luck!