In their basic format, search engines operate on Information Retrieval (IR) principles and are commonly used for search.
Sales AI
June 6, 2024
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For today’s chemical and materials companies, staying ahead of the competition requires more than just traditional sales strategies. As sales leaders seek innovative ways to engage customers and drive revenue, more and more are leaning on tools such as generative AI and retrieval-augmented generation (RAG).
Generative AI has become a buzzword within the sales industry and is leveraged across sectors proving itself to be a game changer for frontline sales teams. Retrieval-augmented generation on the other hand, might not be so familiar — despite it being woven into the fabric of generative AI for years.
What exactly is RAG, and perhaps more importantly, what are its limitations? How does it ultimately impact your chemical sales strategy? Here’s what technical sales teams should know.
At its core, RAG was designed to augment LLMs.
Large Language Models, or LLMs, have been around for years, often trained on vast amounts of internet data. At their simplest, LLMs use natural language understanding (NLU) and natural language processing (NLP) to interpret and find connections between large sources of unstructured data.
LLMs mark a significant step forward in the development of artificial intelligence, but on their own, LLMs lack reasoning. Because of this, LLMs can generate wildly inaccurate or inconsistent answers to prompts — a liability for chemical companies that rely on precise product data.
Enter Retrieval-augmented generation.
Retrieval-augmented generation (RAG) is an AI framework that works in tandem with an LLM to improve the quality of LLM-generated responses. It does this by grounding an LLM model on external sources of knowledge to supplement the LLM’s understanding or association of information.
While the process for users remains the same, the information generated through RAG-based systems is substantially more accurate and reliable. For chemical companies, RAG benefits from access to product specification information, technical data sheets, technical handbooks, standard operating procedures, training presentations, expert knowledge, and more.
As a result, RAG technology has become a pivotal component of generative AI tools across the globe, including Nesh’s Sales AI.
Once a user submits a query, the process unfolds in a two-part sequence — retrieval and generation.
Information retrieval within a RAG-based platform is highly structured, taking into account relevance, recency, and permissions.
Traditionally, the driving force behind RAG retrieval has been keyword matching. A keyword match may have even been how you found your way to this article, with a simple Google search such as “What is retrieval augmented generation?”
Keyword matching is great for industry-specific product names. However, it has its limitations. Spelling errors and the use of industry jargon can quickly yield less-than-optimal results. Because of this, newer tools opt for a hybrid approach incorporating semantic search.
Semantic search capabilities enable searches across multiple languages and modalities by focusing on the semantic meaning of the data, thus ensuring resilience against typographical errors. Think of it as being able to talk to the generative AI system the same way you would a colleague.
Keyword and semantic capabilities equip the user to be hyper-technical in their interaction with the technology. Similarly, they can choose to be more casual in their approach, without forfeiting the quality of information.
Once the relevant information is retrieved, it is seamlessly integrated into the generation model. This produces coherent and contextually appropriate responses to the query. The generation model leverages LLM system understanding and generation capabilities to produce natural language outputs that align with the user's search.
Users benefit from conversational, relevant, and relatable answers that can quickly be turned into action.
For technical sales teams, RAG offers new opportunities to streamline their processes, enhance customer interactions, and drive conversions. Here's how.
RAG empowers sales representatives to deliver personalized, tailored communication to prospects and clients. Sales teams can leverage RAG to generate customized product positioning, product recommendations, and application solutions that resonate with individual stakeholders.
RAG enables sales teams to enrich their sales collateral with up-to-date and relevant information sourced from diverse knowledge bases. Whether incorporating industry trends, case studies, or technical specifications, RAG-equipped systems can dynamically generate information that enhances the value proposition of sales materials, making them more compelling and persuasive.
During technical sales and services interactions, RAG serves as a valuable assistant, providing real-time support and assistance to technical sales and services representatives. By accessing relevant product or application information on-demand, RAG can help address customer queries, objections, and concerns to enhance the overall customer experience.
Chemical teams are facing a wave of retiring chemical experts who often have decades of experience. Replacing these long-term experts is a near-impossible task. RAG paired with expert knowledge capture can help train new technical sales reps by providing real-time access to product knowledge and expertise — all in one place.
First-time adopters gravitate toward RAG chatbots for use on company websites. These tools have a relatively low barrier to entry and can be adopted at any tech stack maturity level. However, the quality of chatbot outputs will depend on the accuracy and quality of the source information.
While bottlenecks rarely occur, RAG chatbots have limitations in complex workflows that require decision-making. Even with the most complete data sources, RAG fails with workflows like product comparisons that require multiple search passes to return a complete result. The complex use cases that deliver value to chemical technical sales teams go beyond RAG’s abilities and require sophisticated, advanced AI design patterns.
RAG is not a one-size-fits-all solution. Both the information inside it and the system design determine how useful it will be for a particular team and use case. The true power of RAG lies in a system designed to adapt to the specific needs of your chemical technical sales team.
At the beginning of this article, we mentioned RAG and generative AI as tools that sales leaders are leaning on more than ever. It can be difficult to separate the two because RAG technology is at the core of GenAI systems.
Think of generative AI as a finely tuned Swiss watch. The user winds up the crown by inputting their question or query. RAG is the mainspring, storing and delivering the energy needed to make the hands tick.
In the case of Nesh, our Sales AI Solutions serve as the face — the user experience where information is relayed. We then layer finely-tuned workflows customized to the domain needs of customers across chemical and materials verticals like coatings, catalysts, additives, and more.
All of Nesh’s technical components work in tandem to deliver accurate and reliable information your team can leverage in everyday conversations, negotiations, presentations, and customer interactions.
Retrieval-augmented generation for knowledge-intensive language processing tasks is a tech asset to technical sales teams. The ability to quickly access and leverage a wealth of information makes it easier for team members to get up to speed and meet customer expectations.
Nesh doesn’t stop here though. We’ve built powerful workflows that enable product positioning, product comparisons, competitive benchmarking, and more.
With Nesh Sales AI, knowledge transfer and access to data are always top of mind. We’ve designed our generative AI platform to help sales leaders streamline learning and revolutionize their sales processes. To learn more about integrating RAG technology and generative AI into your chemical sales process, connect with our team.
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