Building RAG solutions is becoming more common and important in the business world. But there are certain complexities and challenging problems such as:
•How to differentiate between similar-sounding documents
•Rank disparate data sources
•Re-write query to ensure you are
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Key Challenges in Building RAG Solutions for Business
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Preventing Hallucinations in Production LLM Systems
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How can you prevent hallucinations and create more Robust LLM systems? As more and more LLM apps are put into production, the biggest problem to overcome is preventing hallucinations. Here are a couple ways to prevent hallucinations. We personally apply a variety of these
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Free Webinar: Automate Business Tasks with AI Agents
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Transform your business with AI! Join http://
Abacus.AI's free webinar on April 4, 9-11am PDT, and learn how to automate complex tasks with AI agents. Streamline workflows, enhance efficiency, and empower your business. RSVP today https://
eventbrite.com/e/867066357817
?aff=oddtdtcreator
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Abacus AI Applications: Legal Documents, RFP Management, Dashboards
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Here are some interesting applications you can build with Abacus AI: •Legal document generation
•RFP management tools (response generator and summarizer)
•On-the-fly dashboards
•Contract creator
•Customer support and tech support
•Marketing and sales email generator -
Netflix Paper on Cosine Similarity for RAG Systems
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If you want a good read on cosine similarity here’s a fantastic paper by Netflix. It discusses building good RAG systems. Before building these systems, you have to make decisions on chunking, embedding, and algorithms for similarity look-up. You want to ensure you are using
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RAG Complexity: Query Understanding and Document Retrieval Challenges
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RAG is difficult to get right because the system has to understand your query before retrieving the appropriate documents from the vector store. It’s a complex machine-learning problem and you have to solve for multiple parts: query re-writing, efficient retrieval, re-ranking,
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Can LLMs Perform Latent Multi-Hop Reasoning on Pre-trained Data?
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LLMs are good at in-context reasoning; given a bunch of information in context, they can perform complex reasoning tasks and solve problems. But can they perform latent multi-hop reasoning on pre-trained data? Simply put, can LLMs connect and traverse through implicit knowledge
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Abacus AI Agents Now in Production Delivering Instant ROI
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Several organizations use Abacus AI and have pushed our AI agents to production! Our complex AI agents combine structured and unstructured data sources and can automate several tasks showing instant ROI.
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RAG and Prompt Engineering: Preferred Over Fine-Tuning
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If you’re still wondering when to apply either RAG vs fine-tuning, we have a simple answer – always try RAG + prompt engineering first! Fine-tuning only makes sense when you have a domain specific task and doesn’t always result in performant solutions. Additionally, it takes a
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Fine-tuning LLMs: Democratizing AI Performance with Smaugh
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Fine-tuning can change and customize your LLMs to your particular domain, allowing even the GPU poor to improve base models by 10-15%. Additionally, it’s the way for open-source AI to match GPT-4 performance. In the past month, we at Abacus AI have been announcing Smaugh