RAG: Best Practices for Working with an AI Chief of Staff
Jan 11, 2024
Generative Artificial Intelligence (AI) has transformed the landscape of digital interactions by providing fluid and human-like text responses generated by Large Language Models (LLMs). However, the inherent limitations of LLMs, bound by the scope of their training data, often result in responses that, while detailed, may lack the most current and organization-specific insights. This is where Aviso's strategy shines, transcending the traditional barriers of AI with the innovative use of the Retrieval-Augmented Generation (RAG).
Using RAG in Chat Applications
RAG offers real-time, contextual, and evidence-based insights via generative AI, making it more useful than just a standard LLM.
Imagine a B2B sales rep using AI to quickly learn about a potential client. They might input a prompt like, “What are XYZ Company’s main industries, who are their competitors, and how are they doing in the market lately?” The request is converted into a vector and is used to search the vector database, which fetches data relevant to the context of that question. Both this contextual data and the original query are then fed into the LLM, which crafts a text response. This response incorporates its broad but slightly outdated knowledge base and the highly current contextual information.
It's important to note that while training the LLM on general knowledge is resource-intensive, updating the RAG model is much more straightforward. New details about companies, industries, or market trends can be continuously added to the system and transformed into vectors. Additionally, the responses generated by the AI can be cycled back into the RAG model. This helps refine the model since it effectively learns from its past answers to similar inquiries.
The chat now offers information that is not only more current but also more relevant and accurate to the context. In essence, RAG enhances the quality of responses from LLMs.
MIKI: Revolutionizing Customer Interactions
MIKI, powered by Aviso's advanced RAG technology, offers a dynamic, intelligent, and context-aware AI assistant that stands as a testament to Aviso's innovation in Generative AI.
It’s an AI Chief of Staff that identifies trends, provides predictive analysis, automates complex processes, and even recommends actionable strategies based on a deep understanding of current data and past performance.
Built using open-source models like Vicuna, Llama2, and Falcon alongside a vast array of underlying AI/ML/NLP layers, MIKI is Aviso’s home-grown AI Chief of Staff for revenue teams.
While standard AI chatbots provide responses based on static data, Aviso’s MIKI represents a significant leap forward, integrating real-time, company-specific information into each interaction. It seamlessly integrates with your data repositories, from internal CRM systems to external market feeds, making it the central node of your information network. MIKI then digests this information and transforms it into actionable insights, delivering them through an intuitive conversational interface.
MIKI benefits from a hybrid architecture — Generative AI combined with a robust array of underlying AI/ML/Deep Learning layers.
MIKI auto-extracts features and generates predictions and recommendations, using Recurrent Neural Networks, LSTMs (Long Short-Term Memory Neural Nets), Gradient Boosted Methods, Natural Language Processing, Collaborative Filtering and Locality Sensitive Hashing. We also optimize with closed loop attribution and Bayesian Contextual Bandits that model individual user intents and interests.
The content that feeds MIKI’s output comes from Aviso’s Time-Series Database and Knowledge Graphs — which means we only use Gen AI for its capability to retrieve data and understand language.
Because MIKI was built with carefully designed frameworks to handle every aspect of the deal lifecycle, you get contextual insights that actually make sellers and leaders more productive.
These frameworks can further be personalized based on buyer seniority, emotion identified during the call, aspects driving sentiment, and conversation context to ensure the most personalized messaging goes out to buyers.
MIKI leverages both LLMs and Small Selective Models to get the best of both worlds.
Because our open-source models are hosted on Aviso servers, no data leaves our ecosystem and you get zero privacy and security concerns.
What can MIKI do?
✅Enable personalization at scale by crafting customized emails to send to executives at the target company.
✅Perform buyer research letting you know about key buyer events.
✅Alert you to executive departures that may impact getting your deal across the line.
✅Accelerate deal advancement by providing AI-generated next best action recommendations.
✅Inform you of the timing on the ability to close through buyer propensity and intent.
✅Save 20 hours a week for reps by eliminating the need to take notes, update next steps, build mutual close plans, or update your weekly forecast. MIKI does it for you based on your guidance and revenue history.
MIKI by the Numbers: A Quantitative Leap Forward
MIKI ensures that sales teams are not bogged down by the minutiae but are free to leverage their skills where it counts. The data speaks for itself—MIKI is a critical asset for any organization looking to maximize the efficiency and effectiveness of its sales force.
MIKI: Streamlining Time Management and Data Access
Time Savings: Sales representatives using MIKI report a reduction in time spent on administrative tasks by up to 50%, translating to 15-20 additional hours for client engagement and strategic selling.
Data Retrieval Speed: MIKI reduces the time taken to retrieve customer data by 70%, allowing quick responses to client queries and real-time decision-making.
Conclusion
As we continue to harness the power of Generative AI, Aviso remains committed to delivering AI solutions that are not just advanced but also profoundly relevant and uniquely intuitive.
Ready to see how Aviso can transform your business?
Book a demo with Aviso today and take the first step towards unlocking unparalleled AI-driven success.
