Understanding Dynamic Prompts and efficient Data Extraction

đź“… Published on September 6, 2024

I. The power of Dynamic Prompts

Let’s dive into dynamic prompts and how they enhance user interactions with AI models by contrasting them with fine-tuning.

Dynamic prompts are adaptive prompts that adjust in real-time based on user input, providing flexibility without modifying the AI model itself. This approach is particularly useful when additional context or information needs to be provided to the model during interaction, often from external sources like third-party software or Retrieval-Augmented Generation (RAG).

The key difference here is that, with fine-tuning, the model is actually updated — its parameters are retrained on new data to adapt to specific tasks. Fine-tuning alters the model’s internal weights, making it better suited for the particular domain it was trained on.

In contrast, prompt tuning (or using dynamic prompts) keeps the model unchanged, simply modifying the input it receives. This is especially beneficial when the model hasn’t been trained for a specific use case or when you need to integrate real-time, external data into the response — such as information from third-party software, APIs, or Retrieval-Augmented Generation (RAG) — without retraining the model. This allows the model to stay current and adaptable to changing data sources, enhancing its responsiveness and accuracy.

II. Revisiting episode 2: Data and Model requirements

In our previous episode, we discussed the importance of understanding the data entering the model and the desired output. Depending on the model type, strategies for extracting information from user prompts vary:

  • Interactive AI Models: These require swift responses. Combining LLMs with agents and tools for information extraction can be too slow for real-time interactions. While these tools are suitable for data storage and analysis (which we will cover with knowledge graphs and RAG later), they may not be ideal for immediate interactions.
  • Non-LLM Approaches: For tasks that don’t necessitate an LLM, using NLP algorithms or smaller models is often more efficient. Below is a benchmark for selecting appropriate methods based on the information extraction needs.

III. Benchmarking techniques

The golden rule

When possible, utilize CPU-based solutions for good accuracy. These methods are faster and more energy-efficient compared to large LLMs. However, they require more preparation and lack the attention mechanisms found in LLMs, making them suitable for scenarios where quick responses are needed, such as Interaction AI.

Example: Techniques like text classification and feature selection are effective for voicebots needing scripts. They can quickly identify the subject matter and retrieve additional information via backend systems.

IV. Libraries and algorithms for Data Extraction

1. Libraries

  • NLTK: Outdated for advanced tasks and slower than modern alternatives.
  • spaCy, Gensim, Tweepy, TextFeatureSelection: Modern and efficient libraries for various NLP tasks.
  • Scikit learn: Good for classification, clustering, feature extraction&extraction.

2. Algorithms

  • TextRank, YAKE, Rake-NLTK: For keyword and keyphrase extraction.
  • Word2Vec, SBERT: For semantic understanding and contextual embedding.

3. Tip for choosing methods

  • Open context: For scenarios with many possible user inputs, opt for powerful algorithms or libraries.
  • Closed context: For limited user input options, keyword extraction methods are sufficient.

V. Navigating the AI landscape

Dynamic prompts and efficient data extraction are crucial for enhancing AI interactions. By understanding the strengths and limitations of different approaches, you can optimize your AI models for better performance and user satisfaction.

Next Episode: Knowledge Graphs and RAG

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