Retrieval Augmented Generation (RAG) is an important component within now-established AI systems. It provides an in-depth basis of information that can hone the clarity of Large Language Models’ outputs. By bolting onto an LLM, it can produce more trustworthy outputs by double-checking against field-specific, relevant sources.
Large Language Models (LLMs) use transformer-based deep learning to map words as numerical vectors, establishing relationships between them.
But, their reliance on massive datasets creates challenges, such as:
LLMs cannot recognize missing knowledge, leading to:
These flaws undermine AI reliability, as seen when Google Chrome’s AI suggested weak passwords by recommending names and birthdays to be used. To address these risks, you should leverage AI Trust, Risk, and Security Management (AI TRiSM), which is essential for safe AI deployment.
Plus, Retrieval Augmented Generation (RAG) improves accuracy by integrating real-time, authoritative data.
RAGs operate in two steps:
First identifying precisely which topic, field, or industry a prompt is focusing on, the RAG system then uses a search algorithm to access external data beyond the LLM’s original training set.
This data can be drawn from sources like APIs, databases, or relevant documents in a myriad of different formats:
Similar to the basic LLM, the RAG then converts this data into numerical representations within a vector database.
When creating a response to a relevant question, the pre-trained LLM can pull from the RAG’s own vector database to enrich its response. This enhanced context then allows the model to generate responses that are more accurate, detailed, and tailored to the specific user query.
Since RAG can be implemented essentially as a bolt-on upgrade to LLMs, its uptake has been substantial.
Point to an industry that’s exploring LLMs, and there’s a high chance there will be a RAG implemented.
Accurate, succinct, and contextually relevant responses are the core selling point of LLM-powered chatbots; those powered by RAG are able to meet those demands far more reliably, thanks to their ability to pull accurate information from vast company datasets.
This helps realize chatbots’ promised abilities to handle specific customer inquiries or personalized financial advice.
RAG models are able to find legal precedents and summarize relevant case law and documents by finding and retrieving the relevant legal texts. As a result, RAG-enabled LLMs can provide significant time-savings to legal professionals, whilst also helping law students find case-critical information.
In the same way that RAGs are accelerating the process of finding and digesting the correct information for the legal team, security provider-issued LLMs allow security analysts to query and find incidents that are occuring within their tech stack. By ingesting all of the relevant files that a security tool requires and creates, an internal RAG can be an incredible force for cybersecurity efficiency.
It can allow analysts to verify patch implementations, hunt for routes of potential data loss, and search firewall access policies as required.
While RAG offers a new dimension of depth to the responses and data accessible to an LLM tool, it’s not without its own challenges.
RAGs still require an intense amount of data: all of these files and documents need to be incorporated into the RAG, but they’re not inherently usable in this form.
All the usable information in these files still needs to be extracted and chunked up accordingly.
This can bring about a number of pain points, such as:
A core aim of LLMs is to keep costs under control: they’re able to bring value to an enterprise by accelerating employee output, and therefore saving time and money.
However, the processing power this demands can quickly put this aim in jeopardy: context window sizes, training data volumes, and model size all contribute to RAG and LLM costs. This is why it’s so vital to select the LLM that’s specifically tailored to its use-case within your enterprise.
Having already been optimized for that use case, it can enjoy lower inference costs.
The RAG system isn’t infallible: it is able to retrieve data that isn’t fully relevant to the user’s initial query.
This can take a number of different forms, sometimes caused by the system missing top rank documents that may contain the best answer, or the response generation process failing to adequately rank this chunk…
Therefore it fails to make it into the final answer.
Check Point offers RAG support across all 3 of its major security offerings – from firewall-focused Quantum, to Infinity Extended Prevention and Response, and full-stack SASE solution Harmony. Known as AI Copilot, Check Point’s AI agent adapts exclusively to the security data and events that your organization relies on day-to-day.
With AI Copilot in place, security and IT teams are able to ask the assistant to update access controls; create security policies, and resolve tickets. Whatever’s needed in the moment, AI Copilot can rifle through the masses of documentation and event data and deliver mission-critical data to the best person in rapid time.
AI Copilot is far from the only evolution that GenAI will deliver this year: Check Point grants full in-depth security to AI development and deployment projects, identifying shadow GenAI APIs and identifying high-risk sessions and use cases specific to your organization.
Request a trial of our GenAI protection tool if you’d like to level up your AI TRiSM capabilities and stay safe in an evolving world.
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