Custom AI/ML Solutions vs. RAG-Based Agents: Choosing the Right Approach for Your Business
In the rapidly evolving world of artificial intelligence, there are many pathways your business can take to leverage the power of AI. Two key but very different approaches are Custom AI/ML (Artificial Intelligence/Machine Learning) Solutions and RAG-Based (Retrieval-Augmented Generation) Agents.
While both approaches can empower your business to leverage AI for tasks like customer service automation, decision-making, and data analysis, the methods, tools, and timelines involved in setting up these systems are vastly different.
In this article, I’ll explore the key differences between these approaches from an implementation workflow perspective. This might help you decide the best fit for your business needs and inform you of the challenges ahead.
Custom AI/ML Solutions
A custom AI/ML solution is tailored from the ground up to address your specific problem or use case. They are time- and resource-intensive, with numerous iterations over the data (e.g., data cleaning, labelling, feature engineering), model training, and different models being tested.
· Two steps back and one step forward often happen when setting up a custom AI/ML.
15 steps to implement
Let’s look at the basic steps to implement your custom AI/ML solution.
The white % numbers in the diagram represent the average time spent on each step, which can differ significantly by project.
When implementing a custom AI/ML model, stakeholders must understand the nature of implementing a custom-trained AI model.
– The two-steps-back one step forward principle
This is symbolized in the picture by the small arrows moving in the opposite direction.
Data Preparation
1. Collect Data (4%): Gather high-quality data from diverse sources.
2. Clean Data (10%): Ensure the data is free from noise, missing values, and inconsistencies.
3. Label Data (4%): Assign labels to raw data for supervised learning.
4. Vectorize Data (3%): Convert textual and categorical data into numerical representations.
5. Engineer Features (7%): Transform raw data into features that better represent underlying patterns for model training.
Model development
6. Experiment (7%): Try different models, algorithms, and hyperparameters to find the best-performing solution.
7. Train Model (10%): Train the model on the training dataset and optimize its parameters.
8. Evaluate Model (7%): Measure the model’s performance using various metrics and ensure generalization to unseen data.
9. Demo the Model (3%): Present the model to stakeholders or users for feedback or proof of concept.
Deployment
10. Optimize Model Performance (10%): Fine-tune the model for better accuracy, resource efficiency, or faster inference.
11. Deploy Model (7%): Serve the model in a production environment for real-time or batch predictions.
12. Monitor Model in Production (10%): Continuously track the model’s performance and detect predictions or input data anomalies.
13. Detect Data Drift and Concept Drift (7%): Detect when incoming data or relationships between input and output have changed, leading to performance decline.
14. Retrain Model (if necessary) (11%): Adapt the model to new patterns or changing data by retraining on updated datasets.
Governance
15. Model Governance & Compliance: Ensure that the model complies with legal, ethical, and organizational policies.
RAG-based Agent
Rag-based agents leverage existing language models like GPT, so you don’t have to train any model, and a retrieval system that draws information from your organizations structured or unstructured data sources. RAG-based systems and prompt engineering combine the power of retrieval with generation, making them highly suitable for dynamic, knowledge-driven tasks such as customer support chatbots.
12 steps to implement
Let’s look at the basic steps to implement your RAG-based Agent.
Again, the white % numbers in the diagram represent the average time spent on each step, which can differ significantly by project.
In this diagram, I assume that you already selected the LLM; if not, this would need to be added as an additional step.
Although you will need iterations and optimization steps, implementing an RAG-based agent with Prompt Engineering is a more linear process than developing a custom AI/ML.
To make your customer-facing agent respond according to your company’s culture, the engineering and refining of prompts is crucial for getting the best results.
Data Preparation
1. Collect Relevant Corporate Data (15%): Gather relevant corporate data like FAQs, documentation, product information, and policies to support chatbot responses.
2. Clean and Organize Data (10%): Ensure data is structured and searchable, remove duplicates, clean text, and organize into categories or topics.
3. Vectorize Data (10%): Convert text data into embeddings or vectors for efficient retrieval during chatbot interactions.
Model Development
4. Design Retrieval System (10%): Set up a retrieval system to pull the most relevant data when the chatbot receives a query.
5. Engineer Prompts (15%): Create and refine prompts to instruct the language model on accessing and responding using corporate data.
6. Experiment and Refine Prompts (10%): Test and refine prompt-engineering strategies to improve the quality of chatbot responses.
7. Optimize Retrieval and Response (10%): Fine-tune retrieval and language model interaction to optimize response accuracy, speed, and naturalness.
Agent Deployment
8. Deploy the Chatbot (5%): Integrate the chatbot into a customer support platform or website for real user interactions.
10. Monitor and Analyze User Interactions (10%): Track and evaluate chatbot performance through user interactions, identifying areas for improvement.
11. Update Data and Refine Prompts (5%): Continuously update corporate data and iterate on prompt engineering based on new information and feedback.
Governance
12. Governance & Compliance (5%): Ensure the chatbot complies with legal, ethical, and organizational policies.
Which Approach Is Right for Your Business?
Choosing between a Custom AI/ML Solution and an RAG-based Agent depends on your business’s specific goals, data availability, timeline, and budget.
Opt for Custom AI/ML when you have the data and resources to build a solution from scratch and when solving complex, domain-specific problems is critical for your business.
Choose a RAG-based Agent if your focus is on quick deployment and efficient retrieval of existing data, such as customer support automation, without the need for extensive custom development.
Both approaches have their unique strengths. Custom AI/ML systems offer deeper customization and control, while RAG-based agents excel in efficiency, speed, and dynamic data access.
Conclusion
Knowing which approach suits your business needs as AI transforms industries can help you save time, resources, and effort while driving significant value. Whether you choose a Custom AI/ML solution for more complex use cases or an RAG-based Agent for fast, efficient implementation, your decision will ultimately depend on your business’s unique requirements.
Feel free to reach out to give feedback, and if you like this content, you can download a PDF version here.
– Custom AI model vs RAG based agent implementation (Public).pdf