LLM’s Secret Sauce: Not Just Prompts, but Context Engineering

At the recent Xpand conference in Amman, one theme kept surfacing in conversations with fellow individuals in tech: the limitations of large language models (LLMs). 

Many argued that no matter how advanced these models are, they often fail to produce the output you actually want. While that is true in many cases, here’s what struck the most: in most of these discussions, one critical factor was overlooked. Especially by those outside the tech bubble. The real game-changer isn’t just the model itself, it’s context engineering.

If you haven’t been living under a rock, chances are that you have come across the buzzword “prompt-engineering” in the past few years. And yes, while it has been recognized as an official job title in the tech industry, you probably have been practicing it on your own all along. 

The engineering part comes in when you continually modify your prompt to elicit the desired response from an LLM. You either change a word, change the format of your prompt, provide a very specific instruction or piece of information. But prompt engineering can only take you so far.

While it does give you a better output, this isn’t what solely powers our LLM system. Sure, one can argue that what truly powers an LLM is its training data, model architecture, parameters, and carefully engineering user prompts. But in today’s landscape with so many competing open- and closed-source models the real challenge isn’t simply relying on those ingredients. It’s about tailoring these resources to your specific use case so that the responses are not only accurate but also useful.

Many might mistake the interchangeability of the two terms : “context engineering” and “prompt engineering”. While there is a relation, they are two completely different concepts. 

As aforementioned, prompt engineering is carefully curating your prompt to generate a desired output, it is a one-shot process that happens right there and then. Context engineering on the other hand is designing the whole workflow and architecture of the LLM’s thinking process. This is where you define the model’s assumptions, boundaries and specific guardrails. Context engineering consists of several things:

• System prompts
• User prompts 
• short -term memory (the active chat history)
• long -term memory
• RAG (access to external resources/databases)
• Tool access
• Structured output

You may have already realized that prompt engineering is merely a subset of context engineering. 

To put it simply, think of the following analogy:

Prompt Engineering → like the instructions you give to the chef:
*“Make me a spicy pasta with extra garlic.”* Clear, specific wording determines what dish you get.

Context Engineering → like the ingredients you stock the kitchen with:
If you only provide tomatoes, basil, and pasta, the chef can only work within that set. If you add shrimp, cream, and chili flakes, suddenly the options expand.

So the prompt is what you ask the waiter for a dish, while the context is what ingredients the chef already has in the kitchen. 

Food aside, this is exactly what we’ve been building and continuously refining at Meddit.ai. 

In our platform, the LLM context isn’t left to chance; it’s engineered end-to-end for a single purpose: delivering highly accurate, evidence-based responses in healthcare and research.

Instead of treating the model like a generic chef who cooks anything, we’ve designed the “kitchen” specifically for medicine:

• Direct Access to trusted resources such as pubmed and clinical trials so that responses are grounded based on scientific facts. 
• Citation-first outputs ensure every response is grounded in the resource it was derived from.
• Structured context defines boundaries, so answers remain clinical and research-focused.

Memory + RAG pipelines allow for deeper conversations with individual papers.\

This is how we bridge the gap between raw LLM capability and the demands of evidence-based medicine. In short, Meddit isn’t just “using AI”, rather it’s engineering the right context so clinicians and researchers can get an answer they trust. Give Meddit.ai a try today!