Large language models are extremely powerful, but they have a fundamental limitation that often gets
overlooked.
They generate responses solely from their training data and learn statistical patterns, as shown on
page two of the deck.
They do not query live systems, verify facts, or check whether information is current.
This means that from the moment training ends, the model's knowledge begins to age.
It may confidently reference outdated policies, discontinued products, or obsolete procedures.
Worse, when the model encounters gaps in its knowledge, it does not say I don't know.
Instead, it generates the most plausible sounding answer it can.
This leads to three serious problems hallucinations, outdated information, and confidently wrong answers.
Because the language is fluent, Users often trust responses that are factually incorrect.
There is also no inherent source verification claims cannot be traced or audited.
The key insight is critical for engineers to internalize Llms are exceptional language models, but
they are not knowledge databases without external grounding.
They are unreliable as information systems in production environments.
Retrieval.
Augmented generation, or Rag, is an architectural pattern designed to solve the core weaknesses of
standalone llms.
As illustrated on page three of the Dec rag fundamentally changes how models access and use information.
Instead of relying entirely on parametric memory, what the model learned during training, Rag introduces
a retrieval step before generation.
First, the system retrieves relevant documents from a knowledge base, vector database or document
store.
Next, those documents are injected directly into the prompt as context.
Finally, the LLM generates a response grounded in that retrieved information.
This architecture allows the model to combine its natural language capabilities with external verifiable
knowledge.
The result is not just more accurate answers, but answers that can be traced back to specific sources.
The most important takeaway is that Rag is not a prompt trick.
It is a system level design pattern.
By separating language capability from knowledge storage, Rag enables Llms to operate as reliable information
synthesis engines rather than guess based text generators.
Hallucinations occur because llms are trained to predict the next most likely token not to verify truth.
When the model lacks information, it fills the gap with statistically plausible text.
Page four of the deck explains this clearly.
Hallucinations are an inevitable outcome of the training objective itself.
Rag addresses this problem by changing the conditions under which generation happens.
Instead of forcing the model to guess.
Rag supplies relevant factual context before generation begins.
This retrieved context fills knowledge gaps and narrows the model's output space by anchoring responses
to real documents.
Rag reduces the model's tendency to fabricate information.
Claims can be traced back to source material, making verification possible.
The model no longer operates in a vacuum.
It is guided by concrete evidence.
This represents a fundamental engineering shift from guessing to grounding.
With Rag llms transition from creative language generators into controlled information synthesis systems.
While hallucinations cannot be eliminated entirely, Rag dramatically reduces their frequency and impact,
making production use far safer and more reliable.
One of the most serious limitations of standalone LMS is static knowledge.
As highlighted on page five of the Dec training, data is frozen at a specific point in time.
A model trained in 2023 has no awareness of events, policies or product changes that occur afterward.
Traditional solutions attempt to solve this with fine tuning or retraining, but those approaches are
expensive, slow, and operationally complex.
Rag provides a fundamentally better solution by decoupling knowledge from model parameters with Rag
knowledge lives in external systems, databases, document stores, APIs that can be updated independently,
new documents can be added, outdated ones removed, and corrections applied without touching the underlying
model.
This enables real time knowledge access and continuous improvement systems can integrate.
Live data sources reflect policy updates immediately and remain accurate without costly retraining cycles.
For enterprises, this capability is critical.
Business knowledge changes constantly.
Rag ensures AI systems stay current, relevant and aligned with reality rather than locked to a historical
snapshot.
Enterprise environments introduce challenges that make Rag not just useful, but essential.
As shown on page six of the Dec.
Enterprise data is private, proprietary, and domain specific.
It cannot be included in public model training.
Data organizations rely on specialized terminology, internal processes, and institutional knowledge
that exist nowhere else.
At the same time, this information changes frequently.
Policies, procedures, pricing, and regulations may be updated daily.
Standalone llms cannot access this internal knowledge.
Even worse, relying on them without grounding risks, misinformation, compliance violations and loss
of trust.
Wragg bridges this gap by allowing AI systems to retrieve and reason over private enterprise data,
while maintaining security and access control.
Knowledge remains within organizational boundaries, and the model only sees what it is allowed to see
at query time for enterprises.
Wragg is the difference between experimental AI and deployable, trustworthy systems that meet real
business requirements.
Wragg enables a wide range of high impact enterprise AI use cases, as outlined on page seven of the
deck.
One of the most common is internal knowledge assistance, which help employees find information across
wikis, documentation, and policy repositories instantly.
Customer support bots are another major application.
By retrieving from product documentation, troubleshooting guides and historical support tickets.
Rag powered bots provide accurate, context aware responses instead of generic answers.
Compliance and policy search is especially important in regulated industries.
Rag allows employees to query regulatory requirements and internal policies while maintaining auditability
and traceability.
Technical documentation Q&A helps engineers navigate complex APIs and architectures efficiently.
Analytics and reporting assistants use Rag to retrieve relevant metrics and generate natural language
explanations across all these use cases.
One requirement is universal answers must be accurate, auditable, and traceable.
Rag provides the architecture needed to meet these enterprise standards.
Rag is often compared with fine tuning, but they solve very different problems.
As summarized on page eight of the Dec, fine tuning embeds knowledge directly into model weights.
This makes updates costly and risky, especially when knowledge changes frequently.
Rag.
By contrast, keeps knowledge external.
It is easier to update, lower risk, and far more suitable for enterprise environments.
Knowledge can be added or removed instantly without retraining the model.
Fine tuning may still be useful for style adaptation or task specialization, but it should not be the
primary method for injecting factual knowledge.
In most cases, Rag should be implemented first.
The rule of thumb for engineers is simple use Rag before fine tuning.
Rag provides flexibility, traceability, and control, all of which are essential for production AI
systems.
This final slide reinforces a critical message Rag is not optional for production or enterprise AI systems.
As emphasized throughout the deck.
Accuracy, trust and compliance are non-negotiable in real world deployments.
Rag dramatically reduces hallucinations by grounding responses in retrieved verifiable sources.
It enables auditability and traceability, which are required in regulated industries.
Most importantly, it gives organizations control over what knowledge AI systems access and how that
knowledge is used.
At its core, Rag combines three elements llms for language, external knowledge for facts, and system
level control for safety and governance.
This combination transforms Llms from impressive demos into reliable business systems.
The final insight is simple but powerful.
Rag is the bridge between AI potential and enterprise reality.
Without it, llms remain risky and unreliable.
With it, they become trustworthy, scalable, and ready for production use.