Introduction
In the realm of smart agents, prompts function as commands that instruct Large Language Models (LLMs) on the task to be accomplished. They are analogous to commands given to Siri or other AI assistants. LLMs interpret these prompts and produce a completion, which is the output of the task requested.
This process doesn’t occur instantaneously but is a sequential operation. The agent receives a prompt and adds a word to it with the highest probability, influenced by factors such as temperature and top_p. This sequence is repeated until a final completion is generated. Importantly, the LLM considers the entirety of the prompt to determine the next word, highlighting the significance of the context provided in the prompt.
When designing prompts, a clear objective must be articulated to ensure effective completions. In fact, prompt design has become such a specialized task that new roles like “Prompt Engineer” have been created. While one might assume that longer prompts provide more details for better outputs, this is not necessarily true. Excessive information can burden LLM’s memory, which has a token limit. Overloading tokens not only increases costs but can also hinder the completion’s accuracy.
strategies for efficient prompt design:
1. Precision: Be explicit about the task for the LLM.
2. Use separators and other characters: Punctuation and special characters can highlight important sections in the prompts.
3. Provide examples: Provide an example in the prompt to guide the LLM.
4. Naming conventions: Be consistent in the terminology used throughout the prompt.
5. Prompt templates: Use templates for consistency and efficiency.
6. Use Chat to refine your prompts: Chat can help to condense and rephrase prompts.
7. The Playground is your friend: Use the Playground to evaluate and optimize your prompts.
Building a smart agent involves prompt management. One way to handle prompts efficiently is to create a repository to store all prompt templates. These templates can be loaded from databases or JSON files as needed. The smart agent utilizes these prompt snippets based on the LLM’s previous completions, sequentially adding prompts to generate a comprehensive completion. This can be as simple as a state machine or as complex as a web guided by the LLM or another machine learning model.
examples of prompts in a repository:
“`python
prompt_repository.add_prompt(
Prompt_id = “rephrase”,
prompt_template=’\nAgent description: {description}.’
‘\nQuestion: {prompt}.’
‘\nAnswer: {message}.’
‘\nRephrase the answer:’
‘\n’,
start_sequence=”User:”,
restart_sequence=”User:”,
stop=[“\n”, “User: “, “ChatGPT:”]
)
“`
2nd example
“`python
prompt_repository.add_prompt(
Prompt_id = “tell_joke”,
prompt_template=’\nAgent description: {description}.’
‘\nTell a joke about {subject}.’
‘\n’,
start_sequence=”User:”,
restart_sequence=”User:”,
stop=[“\n”, “User: “, “ChatGPT:”]
)
These prompts contain a name, metadata, and the template. The template includes variables that function as inputs. These inputs help transform the templates into prompt snippets. The smart agent memory then incorporates these snippets and sends them to the LLM for processing and completion generation.
If you have any questions or need additional information, please email avidor@ioteratech.com.