Chapter 2: The 5 Step Prompt Engineering Framework
Outcomes
Analyze the differences between prompt engineering, prompt crafting, and prompt fine-tuning in the context of optimizing AI models.
Implement prompt crafting techniques to iteratively improve the quality and precision of AI-generated responses.
Prompt Engineering vs. Prompt Crafting vs. Prompt Fine-Tuning
No matter how you slice it, your results and performance rely heavily on optimization techniques. These are important for producing more meaningful outcomes and enhancing efficiency.
These three optimization techniques often get referenced, starting from the more technical development level to the conversational Prompt Crafting that leads to Fine-Tuning.
Prompt Engineering – Prompt engineering is a problem-solving approach to working with AI. I see this as more of a developer-level role. ChatGPT makes Playground2, a valuable tool for developers to experiment with and understand the behavior of conversational AI. It is highly customizable, requiring some experience to grasp how each setting impacts the language model3.
Prompt Crafting: Over the rest of the article, we will cover Prompt Crafting, which is more for the non-programmer. I look at prompt crafting as Experimentation. Use prompting to get initial results and then make adjustments through practice and experimentation. This is an Iterative Process where you refine your prompts based on the output. Follow where the results take you and pull back where the tool strays from your goals.
Prompt Fine-Tuning – This is the process of aligning the prompts with the precise outcome you want. You take your results and then adjust your prompts until you get the desired results, giving direction to change the results to a more final version. You could also call this Prompt Optimization, which implies you’re working towards the best possible version of your prompts for a task.
The Importance of Succinctness
In prompt engineering, being concise and precise is key. A well-crafted prompt should provide enough information for ChatGPT to understand the user's intent without being too wordy. Striking the right balance between clarity and brevity is crucial.
A well-crafted prompt should be concise and straightforward, providing enough information for ChatGPT to comprehend the user's intent without being overly wordy. However, it's essential to ensure that the prompt isn't too brief, as it may lead to ambiguity or misunderstanding.
Striking the right balance between providing enough information and not providing too much can be challenging. Practicing is probably the best way to master this skill.Great Resource: Prompt Engineering for Effective Interaction with ChatGPT - MachineLearningMastery.com
Avoiding Ambiguity
While it's important to be succinct, ensuring that the prompt is not too brief is equally vital. A prompt that lacks sufficient details can lead to ambiguity or misunderstanding. Finding the sweet spot between providing enough context and overwhelming the model can be challenging but can be mastered with practice.
Mastering the Skill
Crafting effective prompts requires practice. With time and experience, one can develop the ability to engineer concise, informative, and precise prompts. This skill can greatly enhance the performance and usability of generative models like ChatGPT.
Prompt fine-tuning and prompt engineering are crucial in optimizing generative models for specific tasks or domains. The balance between conciseness and clarity in prompts is vital, and with practice, one can become proficient in crafting effective prompts that yield impressive results. Source: machinelearningmastery.com
This is why I like the term Prompt Crafting. It requires skills or expertise developed over time and can be seen as a craft or art accessible to everyone.
No matter what you call it, this Iterative Prompt Calibration is needed as success depends on achieving your desired results step-by-step.
Prompt Fine-Tuning Also For Adding Datasets
Prompt Fine-tuning5 and prompt engineering6 are techniques for optimizing generative models, such as GPT, for specific tasks or domains. Fine-tuning involves training the model on new data sets, while prompt engineering involves crafting better user inputs.
Prompt fine-tuning also involves training the model on new data sets, allowing it to adapt and improve its performance for specific tasks. On the other hand, prompt engineering focuses on crafting better user inputs to guide the model's generation.
Fine-Tuning Transformer-based Language Models
The process following pre-training is known as fine-tuning. A pre-trained transformer model is further trained on a smaller, task-specific dataset. This stage adapts the model to perform particular functions, such as answering customer service questions, engaging in conversational dialogue, or translating languages.
Task Specialization would use Models adapted to specific tasks or domains at this stage. Training involves smaller, more focused datasets, often specific to an industry or application. This creates a model geared toward your end use.
Think of the open-source LLMs4 available from sources such as GPT4All, which have this pretraining completed and are ready for customization. Let’s say you have your company data and want an internal center-of-excellence chatbot. You would then train the model on your dataset, which makes it specialize in your domain.
During fine-tuning, parameters are adjusted to refine the model’s performance for its intended use, enabling it to respond more accurately and relevantly in real-world scenarios.
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Training Phase
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Purpose
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Outcome
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Fine-tuning
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To specialize the model for a specific task or domain
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An adapted model tailored to perform specific functions with higher accuracy
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Footnotes
Prompt Engineering for Effective Interaction with ChatGPT - MachineLearningMastery.com - Great Resource
Transformer Models: NLP’s New Powerhouse (datasciencedojo.com) This Great article has these definitions and a good set of graphics that explain the data flow.
Parameters for LLM Models: A Simple Explanation (linkedin.com)
GPT4All – GPT4All is a repository of free-to-use, locally running, privacy-aware chatbots. You have the ability to use these without a GPU or internet is required. (I will cover this tool in another course)