Building RAG-based LLM Applications
Discover the potential of Retrieval Augmented Generation and LLMs with expert Ambujesh Upadhyay, exploring its applications, challenges, and future.
Hallucination is a common issue that most data scientists face with their large language models, especially those with high complexity. It can occur due to various other factors, such as overfitting and training data bias/inaccuracy, which results in the Large Language Models(LLMs) repeating random facts and outputs. To mitigate this, Retrieval Augmented Generation(RAG) was introduced by Meta AI researchers.
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Start ProjectIndustry expert Ambujesh Upadhyay delves into the complexities of RAG-based LLMs, shedding light on their multifaceted applications across various industries. Amidst this discussion, Ambujesh explains the benefits and challenges associated with leveraging RAG technology, offering insightful recommendations for individuals navigating the dynamic landscape of AI and natural language processing (NLP) roles.
Mitigating LLM Hallucinations : RAG’s to the Rescue
Ambujesh sheds light on a major challenge faced in using large language models : the complex and resource-intensive process of fine-tuning LLMs. This obstacle has made it difficult for data scientists and machine learning engineers to fully leverage the capabilities of LLMs. In response, the Retrieval-Augmented Generation (RAG) framework has emerged as a solution.
RAG tackles the issue of model hallucination by eliminating the need for extensive retraining, thus saving valuable time and resources. By expanding the capabilities of LLMs to specific domains or organizational knowledge bases, RAG enables customization without the need to retrain the entire model. This cost-effective approach enhances the relevance, accuracy, and usefulness of LLM outputs across various contexts.
Foundational Elements of RAG: Embeddings and VectorDB
In his discussion of RAG's core elements, Ambujesh highlights the importance of fundamental consistency in the process. This consistency revolves around embeddings, crucial components that enhance the RAG LLM Model's capabilities. Ambujesh also emphasizes the significance of the Hugging Face open-source library, renowned for its extensive collection of embeddings.
Additionally, Ambujesh underscores the role of VectorDB, a repository for storing embeddings, which facilitates efficient retrieval and utilization during model inference. When prompted to generate a response, the model leverages the stored embeddings in VectorDB to identify semantically similar content. This matching process ensures the RAG framework maintains consistency and accuracy in responses across various contexts and use cases.
Overall, this structured approach enhances LLM capabilities, ensuring the consistency and reliability of generated responses, and contributing to the effectiveness of RAG in practical applications.
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View All ProjectsExploring RAG Use Cases: Understanding How RAG LLM Works
In a practical RAG LLM scenario, Ambujesh tackled the task of creating a Question-Answer system using a standard 11th or 12th-grade biology textbook. The challenge stemmed from the need for context-specific sources, which posed difficulties when using large or proprietary models like GPT-3.5 or LLAMA2.
To overcome this obstacle, Ambujesh implemented preprocessing steps using Natural Language Processing (NLP) techniques. He segmented the biology book text into manageable chunks, applied overlapping methods for comprehensive coverage, and extracted embeddings from the Hugging Face library to construct a custom vector database resembling Facebook's FAISS.
Ambujesh then developed prompts based on the book's context to match specific embeddings within the RAG LLM model. Employing LLAMA2 7B as the underlying model, he crafted prompts to ensure alignment between the provided context and the question, enabling accurate retrieval of relevant information from the vector space. Ultimately, the model leveraged the vector space to deliver precise responses tailored to the inquiries.
Fine Tuning Vs. RAG LLM: Addressing Model Performance Challenges
Ambujesh highlights hallucination as a significant challenge with RAGs, emphasizing the exploration of adjusting critical parameters like temperature to address this issue. Higher temperatures grant the model more freedom, potentially resulting in liberal responses, while lower temperatures encourage adherence to predetermined answers. Despite RAG implementation, instances of suboptimal model performance persist.
Fine-tuning presents an effective method to enhance the performance of language models. However, fine-tuning a large model like Llama2-7B poses challenges due to extensive computing power requirements, rendering local computer usage impractical. Tools such as LoRA and Q-LoRA offer solutions to this issue by enabling fine-tuning of large language models on local computers with limited resources. Instead of fine-tuning the entire model, which is computationally expensive, LoRA and Q-LoRA allow fine-tuning of smaller model segments, significantly improving efficiency and feasibility on regular computers.
Addressing Data Governance Challenges in RAG LLMs
With the future of RAG and LLMs shining brightly, industry experts express concerns about critical issues. Ambujesh sheds light on one of the most pressing: Data Governance. The integrity and confidentiality of data are paramount, especially for tech giants. Ambujesh makes a critical observation regarding the failure of numerous models to meet data governance standards, posing significant challenges.
The absence of comprehensive data governance frameworks could undermine the progress achieved with the RAG LLM model. He emphasizes the need for enterprise-level data governance solutions, highlighting their correlation with the overall efficacy and credibility of AI systems. Ambujesh also references past incidents, such as OpenAI's initial reluctance to disclose complete model details, which sparked legal inquiries and raised questions regarding data transparency and accountability.He anticipates a proactive approach from industry stakeholders, foreseeing the implementation of robust data governance protocols within the next 1-2 years. This would ensure continued progress and ethical practice within AI.
The Shifting Landscape of Job Roles in the Era of LLMS
Developers are encountering new opportunities and challenges as traditional boundaries blur. Ambujesh underscores the importance of adapting to these changes and suggests key skills to excel in this dynamic environment.
For professionals transitioning into data science, proficiency in machine learning and deep learning techniques is essential. However, mastering Natural Language Processing (NLP) fundamentals is becoming increasingly valuable, especially with the emergence of Large Language Models (LLMs). Developers should focus on understanding LLM architectures and how to leverage them effectively for various NLP tasks.
Furthermore, as companies embrace AI adoption and integrate LLMs into their workflows, developers need to collaborate closely with data scientists. This collaboration ensures that models are not only implemented effectively but also fine-tuned and optimized for performance. Developing strong communication and teamwork skills is therefore crucial in this environment.
Those closer to the foundations of NLP should deepen their understanding of NLP algorithms and various LLM architectures. Conversely, developers operating at the application layer should hone their expertise in specific programming languages and frameworks. Adaptability, continuous learning, and collaboration are essential traits for developers witnessing the changing landscape in the era of Generative AI.
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Recommendations for Transitioning into AI Job Roles
Transitioning into roles focused on Artificial Intelligence (AI) demands a strong grasp of NLP related concepts and technologies, especially in light of recent advancements in generative AI. Ambujesh Upadhyay stresses the importance of acquiring knowledge in NLP and Large Language Models (LLMs) and understanding their evolution.
For aspiring professionals, Ambujesh recommends leveraging educational platforms like Coursera, which provide comprehensive courses covering NLP and related subjects. Proficiency in Python is a must, given its widespread use in AI and NLP development. Moreover, Ambujesh suggests exploring specialized tools such as LangChain, offering in-depth insights into NLP concepts, including transformers. Ambujesh also advises individuals to continually update their skills and knowledge through hands-on practical learning so as to stay updated with emerging trends in the industry. Platforms like ProjectPro offer hands-on enterprise-grade data science and big data projects, providing practical learning experiences that enhance problem-solving skills and facilitate the transition into NLP and AI roles.
We trust that this podcast has provided valuable insights into the latest advancements in RAG LLMs, offering a holistic view of challenges, solutions, and career transition guidance. For more top-trending data science content featuring industry experts, subscribe to ProjectPro’s YouTube channel .
About the Author
AmbujeshUpadhyay
He is an Ambitious Data Scientist with core experience in building AI solutions for different platforms using natural language processing, machine learning, data visualizations, and deep learning. I love handling real-life issues and trying to resolve them with current technology. He likes to
