In the arena of AI development, the primary architectural pattern that connects Large Language Models (LLMs) to enterprise knowledge bases is something known as Retrieval-Augmented Generation (RAG). Therefore, RAG pipelines are an essential component in app development planning. A good plan establishes how pipelines will connect proprietary data with LLMs for the best results.
For purposes of clarity, think of a typical LLM as a student taking an exam using only information memorized during classroom instruction. LLMs memorize data during training. Incorporating enterprise data turns the exam into an ‘open book’ transaction in which the LLM is allowed to look at an external data library before formulating an answer.
Why RAG Matters in Product Development
As helpful as base LLMs are, they have their inherent weaknesses. Those weaknesses impact enterprise AI app development in multiple ways. App developers must account for:
- Hallucinations – Base LLMs are more likely to confidently generate incorrect information when they are unsure about how to answer.
- Knowledge Limits – Base LLMs are unaware of real-time data. They cannot utilize new data generated after their training is complete.
- Data Privacy – Base LLMs don’t typically have access to things like internal company documents and proprietary APIs for data privacy reasons.
RAG pipeline integration solves these types of problems without a model needing time-consuming, expensive retraining and model weights. The RAG process injects factual context into a prompt dynamically.
How RAG Pipelines Actually Work
AI app development is a specialty at GojiLabs. They explain that RAG pipelines operate based on a dual-phase strategy of data ingestion and inference. There are four steps involved:
- Ingestion & Chunking – Raw proprietary information is ingested by the system after first splitting it into smaller text segments known as ‘chunks’. These smaller segments guarantee that text fits within a predefined search window.
- Embedding & Indexing – Each data chunk is run through an embedding model that converts text into numerical vectors. The vectors are then stored in a specialized database.
- Semantic Retrieval – A user’s question is converted into a numerical vector. The app then conducts a similarity search to find 3-5 relevant context chunks in the previously mentioned database.
- Context Injection & Generation – The system creates an augmented prompt by combining user input with information received from the database. From that prompt, the LLM can generate an accurate and grounded answer.
Without the RAG pipeline, an LLM is left to form an answer based on the limits of its own memory. Hallucinations are more likely when that memory is insufficient.
RAG Pipelines vs. Model Fine-Tuning
It is possible to get around the hallucination issue by fine-tuning an LLM. On the plus side, fine-tuning teaches a model how to respond using a specific style or format. Fine-tuning is also a way to ensure that an LLM has access to only the information an enterprise wants to make available. But the process also has its downsides.
Fine-tuning takes time. It is expensive and resource-intensive as well. Most importantly, fine-tuning is an ongoing process. It’s not a one-and-done practice. Every time enterprise data changes significantly, a model has to be fine-tuned and retrained.
The main benefit of implementing RAG pipelines is being able to keep an LLM fairly up to date with no need for retraining and fine tuning. But even RAG has its downsides, the biggest one being chunk-size tuning.
In the end, RAG pipelines are the bridge between LLMs and enterprise data. In the world of AI app development, they act as a way to ensure that an application meets enterprise needs without the constant technical load of retraining and updating.
