Curation-Driven Retrieval-Augmented Generation for Personalized Copilots

We are officially in the second half of 2023, and the technology industry seems to be moving faster than ever. The past few weeks saw billions of dollars invested into new AI companies looking to bring AI to every industry. For those building new LLMs, 75-90% of this funding is expected to go towards the purchase and construction of arrays of NVIDIA H100 GPUs that will be used to train all of the different flavors of LLM. This will be great in the long run because it will drive up competition and drive the cost to train models down. However, there is still a problem that corporations around the world are trying to solve: How do they stay ahead and adopt the use of AI, while also maintaining their security standards and keeping their valuable data isolated from the likes of BARD and ChatGPT?

The AI companies that will be able to solve the security problem have a massive commercial opportunity ahead of them. Talented engineers and good data are critical components to making this possible. (This is what we are solving for at Pieces!)

In the exciting world of artificial intelligence, the superhero of natural language processing (NLP) is Retrieval-Augmented Generation (RAG). Think of RAG as a hybrid engine that combines the power of pre-trained models with data from an extensive index, such as Wikipedia, collections of documents, comprehensive codebases, or internal wikis like Notion. This vast amount of resources effectively serves as a super-library at your fingertips.

How Does Retrieval-Augmented Generation Work?

Retrieval-augmented generation works by scanning through a “super-library” based on an input prompt and picking out relevant documents (whatever the model is trained to recognize). These chosen documents then supplement the model's internal memory and, using a technique known as grounding, shape the generated output. The result? An output from the LLM that's not only more accurate and informative, but also reflects knowledge sources that are hyper relevant to the needs of the users, something traditional language models can't achieve.

RAG has showcased its prowess across several tasks requiring a hefty dose of knowledge—be it summarizing vast amounts of information, answering complex questions, or even penning creative pieces. Beyond its capabilities, RAG's adaptability to various NLP tasks and ease of implementation emphasize the necessity to invest in this emerging technology which is paving the way for superior language generation.

As companies figure this out and determine the best way to incorporate AI into their workflow’s, RAG will be the foundation of an organization's internal oracle. Over time, as it learns for the data it is raised on, it will know the answer to any question an employee could throw at it. It will do all of this while maintaining high levels of data security, and authorization permissions. Eventually it will serve information to the user as they need it.

What Makes RAG so Powerful?

Retrieval-Augmented Generation (RAG) is not just another model—it's a breakthrough fusion of pre-trained models and LLMs. This combination equips RAG with several unique strengths, making it a precious asset in NLP.

RAG has a knack for facts. With access to an extensive database, it generates responses that are not only accurate but packed with information. This is crucial for tasks like summarizing or answering questions where precision is vital. This precision is required in many environments where specific data or language is used within organizations and corporations.

Additionally, RAG is the Swiss Army knife of language generation models. Thanks to its broad knowledge base, it can be deployed across diverse NLP tasks, from summarizing and question answering to generating code. This could mean accurate code that is generated in the style of your colleagues and that references relevant resources, not just general boilerplate generated by a LLM.

Lastly, RAG is efficient. The small language model can reuse its internal memory for different prompts and pre-compute and store the index, adding to its operational efficiency. This efficiency leads to lighting-fast results that are more accurate and less costly than generating from a standard LLM.

Beyond these, retrieval-augmented generation also promotes creativity and audience engagement. With access to a wide range of information, RAG can produce results that are engaging and well-tailored to the target audience, ensuring its understandability and applicability.

The Vital Role of Data Collection for RAG Models

Like fuel to a car, data drives the performance of Retrieval-Augmented Generation (RAG) models. Amassing and curating such data can be quite a task—it's time-consuming, costly, and complex. However, the quality of this data directly influences the effectiveness of RAG models, making this endeavor worthwhile. The data selection for RAG models should focus not just on quantity but quality. Some datasets offer more factual and relevant information than others and should be the focus when gathering data.

Despite the challenges, preserving and curating data for RAG models comes with numerous perks. Good-quality data enhances the model's performance, leads to cost efficiencies by allowing data reuse, and promotes collaboration in developing RAG models.

Managing data for retrieval-augmented generation models can be made smoother with certain practices. Using a uniform data format, such as JSON, XML, or CSV, simplifies data storage and sharing. Implementing a metadata schema helps organize and track data, while a data quality framework ensures data accuracy and consistency. A data versioning system, meanwhile, helps track changes over time.

Of course, we must remember to handle data ethically and responsibly, considering data privacy laws and guidelines, and the implications of AI ethics when collecting and curating data for RAG models.

For developers, curation is where Pieces for Developers shines. Pieces allows individuals and teams to maintain a consistent and simple way to curate the code—written, researched, or generated—that they are interested in saving for later. Using in-house LoRA machine learning models, Pieces will enrich those developer materials with a boatload of contextual metadata.

This painless process of enrichment with Pieces eliminates the challenges previously mentioned around the preservation and curation of quality data.

The Future is RAG

The pursuit of collecting and curating data goes hand-in-hand with the growth and efficiency of Retrieval-Augmented Generation (RAG) models. This relationship underscores why individuals and businesses should start building their personalized databases today, ensuring they are well-positioned to reap the benefits of RAG technologies swiftly in the future.

For those working with code, data manifests as snippets of code. Collecting, managing, and securely storing these snippets can be a complex task, which has led to significant growth in users to Pieces for Developers. Whether they are utilizing the Copilot feature, the IDE plugins, the Teams app, or any of the features that Pieces integrates into a developer’s workflow, they are all saving valuable snippets that they want to share, reference or reuse later. All of this will be valuable when grounding their personal or company RAG model.

In a way, these code snippets serve as building blocks, aiding in the design of increasingly efficient, complex, and powerful models. By engaging with data curation now, you're not just preparing for future RAG benefits; you're actively engaging in an intellectual pursuit that contributes to the evolution of artificial intelligence and data science.