RAG is one of the most powerful ways to ground AI in real-world knowledge. This post explains when retrieval adds the most value, how teams apply it deliberately in production, and why control matters for scaling reliable AI systems.
George Nie
December 22, 2025
Retrieval-augmented generation (RAG) has become one of the most important building blocks in modern AI systems.
When teams need accuracy, grounding, and confidence in real-world workflows, RAG is often the fastest way to get there. It turns general-purpose models into systems that can reason over proprietary, fast-changing, or domain-specific knowledge, something base models alone were never designed to do.
At a high level, RAG combines two steps at inference time:
The result is an AI system that can respond using information that is:
● Private
● Continuously updated
● Specific to your organization or users
This approach is now widely recognized as a foundational pattern for grounding LLMs in real-world data. McKinsey describes RAG as a way to connect large language models with external knowledge sources so outputs better reflect the current, domain-specific reality
RAG addresses one of the most common challenges teams face when moving AI into production: control.
Teams adopt RAG because:
● They need answers grounded in internal reality
● Hallucinations are unacceptable in high-stake user-facing environments
● Accuracy and explainability matter more than novelty
Mainstream technical coverage has increasingly highlighted RAG as a practical approach to reducing hallucinations and making AI systems more reliable in production. WIRED, for example, describes RAG as a “software trick” that helps models reference real information rather than inventing answers.
To understand why so many teams are using RAG, it helps to look at how people put it to work.
Many teams deploy internal AI assistants to help engineers, operators, or analysts navigate internal policies, documentation, and decision history.
Because this knowledge is proprietary and constantly evolving, base models alone aren’t sufficient. RAG allows the system to reference the most relevant internal material at the moment a question is asked, and is particularly useful for grounding responses in enterprise knowledge.
Support and operations teams often rely on AI to draft responses or guide decisions using:
● Product documentation
● Policy rules
● Account-specific context
In practice, teams commonly apply RAG selectively:
● Retrieval is used for policy, pricing, or account-related questions
● Simpler conversational replies rely on the base model
This ensures responses remain grounded where precision and accuracy matters most, while keeping interactions fluid and natural.
In regulated environments such as Healthcare, Legal, Finance, Insurance, or enterprise procurement, AI outputs must often be auditable and traceable to source material.
In these cases, RAG isn’t optional. It’s what allows AI systems to operate within real-world constraints by explicitly tying responses back to approved knowledge sources. Otherwise, it introduces tremendous risks of AI hallucination, such as the case for Deloitte’s hallucinated government reports.
Across these examples, RAG delivers the most value when:
● Responses depend on private or proprietary data
● Knowledge changes frequently
● Accuracy, auditability, and traceability matter more than creative flexibility
In these scenarios, retrieval transforms a general model into a domain-aware system, and teams often expand RAG usage as adoption grows.
Some interactions don’t require external context to be effective:
● General reasoning or ideation
● Drafting content with no dependency on internal data
● Simple transformations or summarization
In these cases, teams may rely on the base model and reserve retrieval for moments where it materially improves outcomes. Unnecessary use of RAG in these scenarios may introduce unwanted latency or additional inference cost.
The key distinction isn’t whether to use RAG. It’s when retrieval meaningfully and valuably changes the answer.
As AI systems mature, many teams are evolving from a single static architecture toward a more flexible approach:
RAG becomes something you invoke when it adds value, not something you hard-wire everywhere.
Research and engineering write-ups increasingly highlight this move toward inference-time decisions, where RAG is applied based on the request, workflow, or confidence requirements.
This shift doesn’t reduce RAG adoption. It enables teams to use it more intentionally, with the control of AI inference in mind, and at a greater scale.
RAG is one of the most effective tools available for improving AI reliability and accuracy.
The teams that get the most value from it don’t hesitate to use it, they design systems where control around governance is built in, privacy is ensured, context can be injected when needed, and knowledge sources are scoped deliberately.
Modern inference platforms increasingly support this modular approach, allowing retrieval to be configured and tuned per workflow or project environment rather than locked into a single pattern.
RAG will continue to be central to production AI. The teams that succeed with it will be the ones that treat AI inference (including retrieval) as something they actively engineer and continuously refine, with control in mind.
If you’re exploring using a modular and configurable RAG feature in production, take a look at this 3-minute tutorial on how to set it up in CLŌD. We are here to help ground your AI response and shape your AI inference strategy in 2026, join us and try CLŌD today.
.webp)
.webp)
