LLM hallucinations can be avoided by better understanding of context and user intent, resulting in more relevant data retrieval and more accurate prompts. 

Smarter context injection 

As enterprises deploy Large Language Models (LLMs) in customer-facing and back-office workflows alike, it’s easy to fall into a familiar trap: “We gave the model everything – timelines, tables, logs, and notes – yet it still gets it wrong.”

You LLM responds inaccurately not because it lacks intelligence, but because it lacks precision. Bloated prompts with excessive or irrelevant data confuse the model, increase latency and cost, and raise the risk of LLM hallucination issues. The solution isn’t more data; it’s smarter context injection.

Enter the Model Context Protocol (MCP).  

In earlier posts, we explored how MCP enforces guardrails at runtime, supports real-time harmonization of fragmented data, and optimizes for latency in context delivery. In this post, we focus on how MCP helps construct purpose-aligned, token-efficient prompts that improve both LLM accuracy and governance.

The MCP client-server architecture makes LLMs highly effective in operational use cases like conversational AI for customer service.

MCP works together with a data layer, often accompanied by generative AI (GenAI) frameworks – like Retrieval-Augmented Generation (RAG) or Table-Augmented Generation (TAG) – which integrate real-time enterprise data into LLM prompts resulting in more precise answers to user questions. 

Why LLMs need precision, not overload 

A large language model don’t search through a prompt as a person would. Instead, it relies on internal statistical reasoning over patterns and token context. If the prompt includes too much irrelevant or noisy information, the signal-to-noise ratio suffers. 

Typical symptoms of unstructured or overloaded context: 

• Repetition or contradictions across fields 

• Poor time ordering due to inconsistency or ambiguity 

• Data duplication or entity drift 

• Prompt truncation due to token overuse 

The cost of prompt overload 

Overloaded prompts increase cost and reduce model quality. 

With a leaner, better-scoped prompt, the model is more likely to respond more accurately, quickly, and consistently. When it comes to LLM prompt engineering, precision isn’t just a cost benefit; it’s a quality driver. 

Precision in the MCP pipeline 

MCP’s job isn’t simply to fetch data. It must match the intent of the LLM task to the right subset of enterprise data, and it must also understand the context of that data to represent it faithfully. 

Matching use intent and understanding context involves 2 critical layers: 

1. Intent-aligned selection 
   What is the user asking the model to do? (For example, summarize, recommend, explain?) 
2. Context-aware interpretation 
   What does the retrieved data mean, and is it valid for this use case? 

 To support these layers, MCP relies on: 

• Entity resolution
  Ensuring records are cleanly joined and not duplicated 
• Data quality enforcement
  Validating recency, correctness, and consistency 
• Rich metadata
  Tags for field meaning, sensitivity, time relevance, and system of origin 

MCP precision pipeline 

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The MCP pipeline orchestrates structured context 
based on user intent and data meaning. 

Precision builds on what we explored in our earlier post, “From prompt to pipeline with MCP” – context must be accurate before it can be concise. 

Strategies for prompt precision in MCP 

A well-designed MCP implementation makes precision a priority. Strategies for more precise AI prompt engineering include: 

1. Use structured prompt templates 

   JSON snippets, bullet lists, or question-answer formats help LLMs focus. 

2. Trim irrelevant fields 

   Don’t inject every object property, just the ones relevant to the current intent. 

3. Flatten over-nested data 

   Deep hierarchies confuse language models. 

4. Resolve and deduplicate entities 

   Ensure one clean, consistent representation per entity. 

5. Reinforce chronology and recency 

   Time-sequenced context often improves reasoning. 

6. Cap long histories 

   Inject only the most recent or significant items when context length is limited. 

Intent-to-data alignment 

Different prompt intents require different data scopes and formatting strategies. 

The strategies for prompt precision in MCP tie back to what we discussed in our post, “MCP guardrails ensure secure context injection into LLMs” – precision is not a cosmetic feature; it’s a governance necessity. 

Entity-aware, intent-aligned prompt construction 

The K2view Data Product Platform enables MCP to achieve context precision in real-time. Every business entity (customer, order, loan, or device) is modeled through a data product containing rich metadata including field meaning, priority, sensitivity, and lineage. MCP leverages this data about the data to construct context differently based on the LLM’s intent.

For example, a customer support chatbot might get structured facts and recent events. An AI virtual assistant used for data analysis might get metrics and status summaries. And an escalation generator might get time-stamped records with tone markers. Each prompt is built from clean, filtered, resolved data drawn from live systems but governed by intent.

And, as we covered in our earlier post, “Latency is the hidden enemy of MCP”, all of this happens at the speed of conversational AI.

The result? Lower token count, higher trust, and dramatically better results. 

Discover how K2view GenAI Data Fusion  
grounds prompts with LLM context.