Retrieval-Augmented Generation (RAG) is increasingly becoming a practical pattern for building smarter, safer, and more reliable developer tools. By combining large language models (LLMs) with trusted external knowledge sources, RAG enables applications to generate context-aware responses grounded in real, up-to-date information. In the domains of application security and software development, this approach unlocks powerful use cases that go beyond generic code generation.
At Pervaziv AI, we have experimented with RAG for several months and have seen real benefits in our performance and the accuracy of results from Pervaziv-LLM.
Grounding AI in Trusted Knowledge
One of the biggest risks of using standalone LLMs in security and coding workflows is hallucination which provides confident but incorrect answers. RAG addresses this by retrieving information from curated sources such as secure coding guidelines, internal security policies, vulnerability databases, or approved code repositories before generating a response. This grounding ensures that recommendations align with organizational standards and known best practices, which is especially critical in security-sensitive environments.
For example, when developers ask about input validation or authentication patterns, a RAG powered system can pull from authoritative sources like internal frameworks or vetted security sources and playbooks. This reduces reliance on generic advice and helps ensure consistency across projects and teams.
Secure Coding Practices
RAG can be embedded directly into developer workflows to promote secure-by-design coding. When a developer writes or reviews code, the system can retrieve relevant secure coding standards, known vulnerability patterns, or historical issues from similar components. The generated output can then explain potential risks, suggest safer alternatives, or highlight missing controls.
Because the retrieved context is specific to the language, framework, or organization, the guidance feels practical and secure rather than theoretical. Over time, this reinforces good security practices and reduces the likelihood of common vulnerabilities such as injection flaws, insecure deserialization, or improper error handling.
Context-Aware Code Reviews and Analysis
Traditional static analysis tools often flag issues without explaining the “why” behind them. RAG-enhanced systems can bridge this gap by combining scan results with contextual documentation and examples. When a security finding is detected, the system can retrieve explanations, remediation steps, and code snippets tailored to the exact issue.
This approach improves developer understanding and speeds up remediation. Instead of searching across multiple tools or documents, developers receive a consolidated, context-rich explanation that connects the vulnerability to both theory and practice.
Vulnerability Management and Threat Awareness
In application security, staying current with emerging threats is a constant challenge. RAG can retrieve information from vulnerability feeds, advisories, and internal incident reports to provide timely, relevant insights. When a team asks about a specific library or component, the system can surface known vulnerabilities, affected versions, and recommended mitigations.
This capability is particularly valuable during incident response or risk assessments, where speed and accuracy matter. By grounding responses in the latest retrieved data, RAG helps security teams make informed decisions without relying solely on manual research.
Enhancing Developer Productivity
From a coding perspective, RAG enables AI assistants to be more than autocomplete tools. When developers ask how to implement a feature, the system can retrieve internal APIs, architectural guidelines, or past implementation examples. The generated code is then aligned with existing patterns and security requirements.
This balance between productivity and control is key. Developers move faster, but within guardrails defined by trusted sources. As a result, teams reduce rework, avoid insecure shortcuts, and maintain architectural consistency across applications.
Reusing Organizational Knowledge
Many security and development insights already exist within an organization but are scattered across documents, support tickets, and repositories. RAG acts as a unifying layer that makes this knowledge accessible at the moment it’s needed. Instead of reinventing solutions or repeating past mistakes, teams can learn from historical context and institutional experience.
Over time, this creates a feedback loop: as more secure patterns and lessons are documented, the RAG system becomes more valuable, continuously improving the quality of guidance it provides.
Pervaziv AI
As shown in the reference figure above, we have built the RAG workflow after careful analysis and benchmarking performance of several open and closed source LLMs. We have built a large vulnerability dataset that is vetted for security issues. We pre-process the dataset and create a large embedding dataset with the help of very well performing embedding models.
All of the intelligence is built into our backend and end users are unaware of the complexities of the underlying stack. Users interact with Cortex through VSCode or Visual Studio for coding and security related queries. Internally, Cortex and Pervaziv-Backend and Pervaziv-Utils do all of the work to query the embedding DB, creating intelligent prompts, and validating prompts with this extensive knowledge base.
We then do our magic with Pervaziv-LLM which has additional enhancements when compared to many of the standard models such as Gemini, OpenAI, Llama and Claude models. We provide better answers than all of these models put together. Once the processing is complete, we respond back to the user with our validated response with a very high probability of acceptance by the user.
We announced the availability of RAG in our blog post back in April of 2025: https://pervaziv.com/blog-availability-of-rag-sca-and-sbom/. Since then, we have made several improvements to make our LLM responses more accurate and relevant. We will detail our benchmarks in this area in a future blog.
Conclusion
RAG represents a practical evolution in how AI is applied to application security and coding. By grounding language models in trusted, relevant knowledge, it delivers more accurate, context-aware, and secure outcomes. Whether improving secure coding practices, streamlining code reviews, or enhancing vulnerability management, RAG helps organizations harness AI’s strengths while mitigating its risks.
As coding and security continue to converge, RAG-based systems are well positioned to become foundational tools in modern software engineering. At Pervaziv AI, we are squarely at the intersection of developer and security tools and are aptly suited to make significant strides in both these areas.
