Overview
AI is becoming the backbone of modern business operations. From generating compelling marketing content to automating customer service, AI transforms how companies compete and succeed. The genie is out of the bottle, and businesses that leverage AI effectively will gain significant advantages over those that don’t.
While large organizations have dedicated teams building custom AI solutions, what about startups and small businesses? Smaller organizations should be able to leverage AI's power without the unlimited resources of a colossal enterprise.
We’ll discuss Retrieval Augmented Generation (RAG), which acts as a bridge to connect AI models to your business data. RAG makes AI responses more accurate and tailored for your team and doesn’t require significant upfront investments in model fine-tuning. This makes RAG a great option that balances personalization, performance, and costs.
In this primer, we'll walk you through everything you need to know about implementing RAG for your business:
- Why out-of-the-box AI models fall short for specific business needs
- Essential RAG concepts explained
- Common challenges (and how to overcome them)
- Best practices for RAG implementation
RAG Use-Cases for Startups and Small Businesses
Here are some typical use cases to demonstrate what you can do with RAG-based applications:
- Speed up content creation: You can create new blog and video posts, using your old content to ground the AI in your tone and voice.
- Customer service agents: By supplying your support articles, warranty terms, and return policies, your customer service AI agent can help with ticket triage and respond to customers quickly.
- Onboard faster: Recently hired freelancers or employees? You can ground an onboarding AI on your playbooks, SOPs, and CRM data to answer questions about product updates, standard workflows, and other procedures without burdening yourself or senior staff.
We believe AI will empower owners and employees, not replace them. The true value-add for AI systems is saving time and reducing repetitive tasks so that you can focus on the highly creative and non-scalable tasks.
Why RAG?
AI systems are so advanced now that they can perform complex, multi-step tasks, such as automating complex workflows. However, using out-of-the-box AI models has limitations when creating new AI-powered products, services, or operations:
Knowledge Limitations
AI models may not be explicitly trained to answer your question or request. This creates several problems:
- Hallucinations: Models sometimes generate plausible-sounding but incorrect information without the proper context. Hallucinations can mislead customers or your team, eroding trust in the AI system.
- Outdated information: Models trained on old, static data won’t know about recent events, product updates, or industry changes, which hurts your users or employees using your models.
- Knowledge gaps: Out-of-the-box AI models tend to perform poorly in niche domains or with company-specific terminology and processes. This limits the usability of your services and products.
Integration Challenges
It takes more than uploading documents to an API to use AI to its full potential within a company. Some operational issues include:
- Scattered data: Businesses often have data spread across platforms, devices, and databases. AI models are data greedy and need access to everything for more accurate responses.
- Compliance concerns: AI may generate content that violates regulatory requirements or your company's policies. Sufficient guardrails must be built to protect your business from these liability issues.
- Limited traceability: AI systems are complex, and identifying and fixing the root cause can be difficult. Proper tracking systems should be implemented to debug and audit your AI applications.
User Experience Problems
Finally, other issues can hurt the user experience:
- Context constraints: You can only feed a model so much information in a single query.
- Fine-tuning costs: Fine-tuning models on company data can be prohibitively expensive for startups and small teams.
- Response consistency: Without a way to ground the model, responses to similar queries can vary significantly, eroding user trust in an AI system.
RAG improves model responses by connecting existing AI systems to your company’s knowledge base. This leads to more accurate and personalized resources specific to your users and organization.
What is RAG?
RAG is like giving your AI an open-book test: rather than relying on its memory, the model can access the information it needs to provide the best answer.
There are three steps in RAG:
- Retrieve relevant facts from your platforms, databases, and devices
- Augment the user’s query with this context
- Generate a response grounded in verified facts
RAG is embedded in many custom AI applications. For example, many e-commerce companies use RAG-based solutions to create a customer AI agent who handles customer service needs using product documentation, pricing sheets, and return policies. Companies save a lot of money by reducing their customer service staff, and these AI solutions also increase customer satisfaction by providing instant responses 24/7.
How Does RAG Work?
RAG is a powerful AI tool with many moving parts that can be optimized to provide better AI responses. We discuss the essential components your team needs to consider to make informed decisions when building or choosing RAG-based systems.
Embeddings
AI models don’t see words on a screen: an embedding model converts text into a numerical representation called an embedding, which captures the meaning and context of the document. These embeddings are the key objects RAG uses to enrich user prompts with relevant information.
Selecting an embedding model is a critical decision. Solid embedding models will provide smooth customer experiences, while weak embeddings result in jumbled context retrieval and confusing model responses.
Major AI model providers like Cohere provide pre-trained embedding models, which work well for many applications. However, if the AI’s responses are inadequate to serve your company or customers, embedding models is easier and cheaper to fine-tune than whole LLM systems. Fine-tuning embedding models provides a massive opportunity to improve your AI system's components.
Chunking documents
AI models can only process so many words at once. We need to break up long documents into smaller, digestible chunks for the AI model.
There are many options for chunking, and they impact the model’s context. Simple chunking methods like semantic chunking, where you break up text at natural boundaries (e.g., paragraphs), offer a good balance of simplicity and effectiveness.
However, chunks can be taken out of context. Let’s take the following product description as an example:
"Product X requires calibration every 90 days. However, if used in high-humidity environments, calibration should be performed monthly. Failure to calibrate properly will void the warranty."
If we perform sentence chunking, the AI will miss important context that can void their warranty. These situations are not rare, and you should thoroughly evaluate your chunking strategy to get the best results.
Retrieving documents
To connect everything we have covered, we first need to convert your company’s data into embeddings, splitting longer documents into smaller chunks before this transformation. Then, we store embeddings in a vector database, an optimized storage solution for LLMs to retrieve your documents.
Now we move on to the retrieval component. How does your AI system select the most relevant information related to your query?
When someone asks the AI a question:
- Your RAG system searches a vector database containing the embeddings of your business’s data.
- A retriever model calculates a similarity score between the question and candidate embeddings to find the chunks most relevant to the question.
- Finally, the candidate embeddings are ranked, and the retriever selects the best matching embeddings to inform the AI’s response.
Here, we must consider essential tradeoffs between retrieval accuracy, latency, complexity, and costs. In general, adding more components to our retriever that improve the similarity score and ranking of relevant chunks allows us to feed the LLM more accurate context. However, this increases the system's latency and costs because we’re performing more computational operations, and it can make maintaining the system more difficult.
Your ability to modify the retriever will vary based on your chosen RAG platform. Coding up a RAG system with LangChain enables you to optimize this important component from the ground up. Other vector database solutions like LlamaIndex, Weviate, and Pinecone will provide some customization while reducing development time. In contrast, many no-code solutions don’t enable any modifications but will let you hit the ground running.
Finding the right balance of RAG performance and resource allocation is crucial for startups and growing businesses. We recommend starting with a minimum viable approach that delivers value to customers and continuously improves.
Augmentation and Generation
Data preparation (embedding and chunking) and retrieval are the foundations of RAG. However, additional augmentation steps can improve the generation of high-quality responses for your customers and stakeholders:
- Filtering: The LLM may give information weighted towards duplicated, highly similar chunks. This may overemphasize specific points while neglecting important context, diminishing the user experience.
- Structuring and formatting: Labeling the retrieved documents in the JSON object (e.g. {pricing_policies: [policy 1, policy 2, policy 3, …]}) guides the model towards more relevant responses.
- Compression: Model provider APIs charge per token, and longer chunks result in higher costs and latency. Summarizing and compressing the context further with NLP techniques or cheaper LLMs can reduce costs and lead to more concise responses.
- Attribution and citation: When customers can see what sources the AI pulled, they are more likely to trust the model response, assuring fears about hallucinations and model errors.
While these enhancements may require additional investment, they can improve customer satisfaction, reduce issue submissions, and align the responses to fit your business needs.
Challenges with RAG
While RAG has many powerful capabilities that improve the customer experience, there are technical hurdles to overcome. Here are three common hurdles businesses face and how to address them.
Fragmented Data
Many teams have critical information scattered across multiple tools and platforms, such as Slack, Google Drive, and Notion. Dispersed data makes it impossible for AI to access your most valuable insights.
We recommend consolidating the most important documents into a unified knowledge base (e.g., Google Drive, AWS S3) to ensure you can create a solid foundation for your RAG application. As your needs grow, you can establish additional data connections to create a seamless data ecosystem.
Retrieval Accuracy
While RAG systems help AI models give more relevant responses, they can still underperform without proper tuning. It is essential to ensure your RAG application doesn’t miss crucial context or overemphasize specific points.
We described several components that can be tuned to optimize RAG performance, from selecting the right embedding models to implementing different chunking strategies. To maximize your RAG application's performance, you can work with an AI engineer to shorten the development time and enable your team to focus on the critical things: getting customers.
Evaluation Metrics
AI systems can be costly to run, and it is important to measure the impact RAG services have on improving business outcomes.
Start simple but specific—measure customer satisfaction scores and error reduction rates for customer-facing applications or track task completion time for internal tools. These initial metrics provide immediate validation while laying the groundwork for more sophisticated analysis. AI development partners can help design comprehensive evaluation systems demonstrating return on investment, ensuring your AI initiatives are moving your business forward.
By thoughtfully addressing these challenges, you can transform RAG from a promising technology into a genuine competitive advantage without the extended learning curve typically associated with cutting-edge AI implementation.
Conclusion
RAG offers startups and small businesses an opportunity to create AI applications that understand your specific business context without massive investment and technical expertise. Connecting AI models to your company’s data allows you to get responses that are accurate, relevant, and aligned with your business’s needs.
Implementing RAG requires some technical work, but the benefits make RAG an excellent option for small teams looking to work more efficiently.
Need help implementing RAG and other AI solutions for your business? RAG is a useful framework for making your data available for AI. However, designing an effective system requires extensive planning, experimentation, and optimization.
Our experts at Torchstack have expertise across the entire software, data, and AI lifecycle. We have served small businesses and startups since 2017, starting projects from proof-of-concept to launch in various industries such as logistics and supply chain management to healthcare.
Contact us today for a free consultation and a customized action plan for your specific needs.