Understanding How AI Text Generation Works: Prompt Engineering, Inference, and In-Context Learning
Prompt, inference, and completion — these are key terms in AI text generation that might sound complicated, but they’re easier to understand than you think. Let us explore them!
Prompt: This is the text you give to the AI model to start with. Think of it as the question or task you’re asking the model to handle.
Inference: This is the process where the AI takes your prompt and works to generate a response.
Completion: This is the output text the AI generates in response to your prompt.
One of the most important aspects of working with AI models, especially large language models (LLMs), is prompt engineering. This means tweaking and improving the prompt you give the AI to get better results. You might need to revise your prompt several times to get the AI to produce the desired outcome.
A powerful technique in prompt engineering is in-context learning. This involves including examples of the task you want the AI to perform within the prompt itself. For instance, if you want the AI to classify the sentiment of a movie review, you can include a few examples of positive and negative reviews within the prompt.
Types of Inference
∘ Zero-Shot Inference: Here, you provide the AI with a task without any examples. For example, you might simply ask it to classify a review as positive or negative. The largest LLMs are pretty good at this, even without examples.
∘ One-Shot Inference: This is where you provide one example of the task you want the AI to perform. For example, you might show the AI one positive review and then ask it to classify another review. This helps smaller models understand what you want them to do.
∘ Few-Shot Inference: Here, you provide several examples. For instance, you might include both positive and negative reviews in your prompt. This can help even smaller models understand the task better and generate more accurate completions.
Handling the Context Window
The context window is the total amount of text (both your prompt and any examples) that the AI can handle at once. If your model isn’t performing well with several examples, it might be time to consider fine-tuning. Fine-tuning means training the model further with new data specific to your task, making it more capable of handling your specific needs.
The Importance of Model Size
The performance of AI models often depends on their size, specifically the number of parameters they have. Larger models, like those with billions of parameters, are usually much better at zero-shot inference. They can understand and complete tasks they weren’t specifically trained for. Smaller models, on the other hand, might need more guidance and examples to perform well.
Choosing the Right Model and Settings
When working with AI, you might need to try a few different models to find the one that works best for your specific use case. Once you have a model that works well, there are various settings you can adjust to influence the style and structure of the AI’s completions.
How to Control AI Text Generation: Inference Techniques and Parameters
In this lesson, I’ll explore methods and configuration parameters you can use to influence how an AI model decides the next word to generate. If you’ve used large language models (LLMs) on platforms like Hugging Face or AWS, you might have seen controls for adjusting how the LLM behaves.
Key Configuration Parameters for AI Models
When you interact with LLMs, you have access to certain configuration parameters. These are different from the parameters used during training. Instead, they are applied during inference (the process of generating text) and give you control over aspects like the maximum number of tokens in the completion and the creativity of the output.
∘ Max New Tokens: This parameter sets a limit on the number of tokens the model can generate. For example, setting it to 100, 150, or 200 limits the model accordingly. Note that the model might stop earlier if it predicts an end-of-sequence token.
∘ Probability Distribution: The model’s softmax layer outputs a probability distribution for each word in its vocabulary. This determines the likelihood of each word being the next one generated.
Decoding Methods
Different methods can control how the AI selects the next word:
Greedy Decoding: This simplest method always picks the word with the highest probability. While effective for short texts, it can lead to repetitive or unnatural text.
Random Sampling: This method introduces variability by selecting words randomly based on their probability. It can prevent repetitive text but might produce overly creative or nonsensical outputs.
Top-k Sampling: This limits the model to choosing from the top k words with the highest probability. For example, if k is set to 3, the model picks from the top 3 words, adding randomness while maintaining sensible choices.
Top-p Sampling: Also known as nucleus sampling, this method limits the model to words whose combined probabilities don’t exceed p. For example, if p is 0.3, the model picks from words whose probabilities add up to 0.3, balancing randomness and relevance.
Temperature Setting
The temperature parameter adjusts the randomness of the output:
∘ Low Temperature (< 1): Concentrates the probability on fewer words, leading to more predictable and less random text.
∘ High Temperature (> 1): Spreads the probability more evenly across words, resulting in more creative and varied text.
∘ Temperature = 1: Uses the default probability distribution without any alteration.
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