This section examines various optimization techniques, highlighting their objectives, ideal use cases, and inherent trade-offs, particularly in light of balancing the business goals outlined above.
Table Breakdown Techniques:
Implementation complexity: low
When to use: For modeling responses and quick improvements without modifying the model. Start with this technique to maximize the effectiveness of a pre-trained model before trying more complex optimization methods.
What it includes: Prompt engineering involves crafting the input query into the model in a way that produces the desired output. This requires understanding how the model responds to different types of instructions but does not require retraining the model or changing its architecture. This method only improves the way the existing model is accessed and applies its pre-trained knowledge, and does not enhance the model’s internal capabilities.
“It’s like adjusting the way you ask a question to a knowledgeable friend to get the best possible answer.”
Examples:
- Asking a language model to respond to “write a poem in the style of Shakespeare” versus “write a poem” in a specific literary style.
- Instantly providing a detailed scenario for conversational AI to ensure the model understands its role as a customer service agent.
The trade-off:
- Trial and error: Designing highly effective prompts requires iteration, as the relationship between the prompt and the AI ​​output is not always intuitive.
- Output Quality: The quality of the output depends heavily on the design of the prompt, and there are limits to the level of improvement you can achieve with this method.
Implementation complexity: Medium
When to use: Fine-tuning should be considered when you need the model to adapt to a specific domain or task that is not well covered by the base pre-trained model. This is a step towards increasing domain-specific accuracy and building more specialized models that can handle domain-specific data and terminology.
What it includes: Fine-tuning is the process of continuing to train a previously trained model on a new dataset that is representative of the target task or domain. This new data set consists of input-output pairs that provide examples of the desired behavior. During fine-tuning, the model weights are updated to reduce the loss on this new dataset, effectively adapting the model to the new domain.
“Think of it like giving your friend a crash course in a topic you want them to become an expert in. Showing them several examples of questions that might come up on the exam and sample answers with which to expect them to respond. is done
Examples:
- A general-purpose language model can be fine-tuned to legal documents to improve its performance for evaluating such documents.
- Image recognition models can be fine-tuned with medical imaging data sets to better identify specific diseases in X-rays or MRIs.
The trade-off:
- Data Required: Fine-tuning requires a labeled dataset that is relevant to the task, which can be resource intensive to create.
- Risk of overfitting: Fine-tuning the model has a potential risk of over-specializing the data, which may reduce its ability to generalize to other contexts or data sets.
Implementation complexity: High
when For use: RAG should be considered when an AI model needs to access and incorporate external information to generate answers. This is particularly relevant when the model is expected to provide up-to-date or highly specific information that is not included in its pre-trained knowledge base.
What it includes: RAG combines LLM’s creativity with retrieval systems. A retrieval system queries a database, knowledge base, or the Internet to find information relevant to an input prompt. The retrieved information is then fed to the language model, which incorporates this context to produce a richer and more accurate response. By referencing the sources used by the RAG system to generate answers, generative AI applications can offer better explanations to users.
In the coming years, this optimization technique is expected to gain widespread popularity as an increasing number of products try to adapt their latest business data to the experiences for customers.
“It’s like having your friend look up information online to answer questions that are outside of their immediate expertise. It’s an open-book test.
Examples:
- In a RAG-based online chatbot, the retriever can pull relevant information from a database or the Internet to provide up-to-date responses.
- Homework Assistant AI can use RAG to retrieve recent scientific data to answer a student’s question about climate change.
The trade-off:
- Complex implementation: RAG systems require a well-integrated recovery system, which can be difficult to configure and maintain.
- Information Quality: The usefulness of the generated response depends on the relevance and accuracy of the retrieved information. If the retrieval system sources are outdated or incorrect, the responses will reflect this.
- Slow response time: Obtaining information from external sources to generate a response can increase latency.
Implementation complexity: Very high
When to use: RLHF should be used when model results need to be closely aligned with complex human decisions and preferences.
What it includes: RLHF is a state-of-the-art reinforcement learning technique that improves model behavior by incorporating direct human evaluation into the training process. This process usually involves collecting data from human operators who rate the outputs from the AI ​​on various quality metrics such as relevance, helpfulness, tone, etc. These data signals are then used to train a reward model, which guides the reinforcement learning process to produce the output. which are more closely aligned with human preferences.
“It’s like learning from your friend’s past interactions that made the discussion enjoyable, and using that knowledge to improve future interactions.”
Examples:
- A social media platform can use RLHF to train a moderation bot that not only identifies inappropriate content but also responds to users in a way that is constructive and context-sensitive.
- A virtual assistant can be fine-tuned using RLHF to provide more personalized and context-aware responses to user requests.
The trade-off:
- High Complexity: RLHF involves complex, resource-intensive processes, including human feedback collection, reward modeling, and reinforcement learning.
- Quality risk: There is a risk of bias in feedback data, which can affect model quality. Ensuring a consistent quality of human feedback and aligning the reward model with the desired results can be difficult.
Implementation complexity: Moderate to high
When to use: Knowledge distillation is used when you need to deploy sophisticated models on devices with limited computational power or in applications where response time is critical.
What it includes: It is a compression technique where a smaller, more efficient model (called the student) is trained to mimic the performance of a larger, more complex model (the teacher). Training goes beyond simply learning the correct answers (hard goals), and involves the student trying to generate probabilities similar to the teacher’s predictions (soft goals). This approach enables the student model to capture important patterns and insights that the teacher model has learned.
“It’s like distilling the wisdom of a seasoned expert into a short guidebook that a novice can use to make expert-level decisions without going through years of experience.”
Examples:
- A large-scale language model can be distilled into a small model that runs efficiently on smartphones for real-time language translation.
- Image recognition systems used in autonomous vehicles can be distilled into a lightweight model that can run on the vehicle’s onboard computer.
The trade-off:
- Performance vs. Size: The distilled model may not always match the performance of the teacher model, leading to a potential loss of accuracy or quality.
- Complexity of training: The distillation process is time-consuming and involves careful experimentation to ensure that the student learns the model effectively. This requires a deep understanding of the architecture of the models and the ability to translate knowledge from one to another.
Now let’s take a look at a real-world example in practice.