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The world of artificial intelligence is evolving faster than ever. With the rise of generative AI, large language models (LLMs), and an ever-expanding toolkit of AI systems and AI models, we now have the power to solve increasingly complex tasks using natural language interfaces. But here’s the thing: the success of your AI outputs doesn’t solely hinge on the model you choose. It hinges on how you use it.
Whether you’re using Chat GPT for quick ideation, or orchestrating domain-specific solutions with OpenAI’s GPT-4 or Microsoft’s Azure OpenAI offerings, the question remains…
How do you pick the right AI models for your workflow?
At Momentum, we’ve built and refined a step-by-step approach that helps us consistently deliver accurate, useful, and adaptive AI responses. We call it our AI Brain Framework, and it’s built on three pillars: the foundational model, context engineering, and prompt engineering.
In this post, I’ll walk you through our framework, share real-world use cases, and show how it enables better problem-solving, regardless of the complexity of the task or the size of your datasets.
The Three Pillars of Momentum's AI Brain Framework
1. The Foundational Model: Your AI Brain
Let’s start with the obvious: the choice of LLM matters. Whether you’re using GPT-4, Claude, Gemini, or an open-source alternative, the foundational model is the engine behind every AI interaction. But here's the kicker: most top models today are converging in capability. The gap between leading and mid-tier models is shrinking.
So the real question becomes: how good does the model need to be for the job?
The answer hinges not just on task complexity, but on three key factors:
- Creativity required: If you're drafting templated content or summarizing meeting notes, a smaller model may suffice. But if you're producing original thought, like writing a long-form analysis from an account brief, higher-end models are far more effective.
- Context window: Tasks involving large inputs (e.g., long transcripts, documents, or research bundles) tend to perform better on models with expansive context capabilities and better information retrieval under compression.
- Multimodality: If your workflow involves more than just text like ingesting and interpreting audio, images, or video, only the latest multimodal models will do the job well.
Ultimately, knowing when to optimize for model quality versus other pillars like context engineering or retrieval infrastructure is key. The model is your brain, but it needs the right body and tools to reach its full potential.
2. Context Engineering: Feeding the Brain
This is where things start to get interesting.
Every AI model, no matter how advanced, is only as good as the background information it receives. That’s why context engineering, the process of supplying the most relevant context, is a game-changer.
Say you’re using Chat GPT to understand sales meetings and figure out whether the next call is scheduled. You could dump the entire transcript into the prompt, sure. But what if it’s ten hours of audio?
Instead, we build a retrieval-augmented generation (RAG) system that intelligently surfaces the right context snippets:
- "Meeting is booked for next Friday"
- "Client confirmed availability on April 28th"
By providing tight, relevant context, we reduce the cognitive load on the model and allow it to perform better, even if it's not the most powerful model available.
This is where our internal tooling at Momentum shines. Our proprietary Context-Building Engine does this automatically, ranking and selecting the most relevant chunks from historical conversations, documents, or datasets.
3. Prompt Engineering: Asking the Right Questions
If context is the food, then the prompt is the recipe. The model doesn’t just need to know what it’s working with, it needs to know what you want out of it.
This is where prompt engineering techniques come into play. Whether it’s:
- Chain-of-thought prompting for complex reasoning,
- Templates for consistency,
- Or step-by-step decomposition for intricate tasks,
A well-structured prompt can transform mediocre outputs into gold.
Prompt engineering is not just for developers or researchers anymore. It’s an essential tool for anyone working with generative AI. And like any skill, it improves through iterative refinement. You try, analyze, adjust, repeat.
Real-World Use Case: Extracting Next Steps from Transcripts
Let’s look at a simple, high-impact example.
Imagine you’ve got sales call transcripts and you want to:
- Determine if a next meeting is booked
- Extract the date of the next call
- Summarize follow-ups
You could throw everything at a model and hope for the best, or you could get strategic:
- Use our context engine to pull sentences like: "Looking forward to seeing you next Thursday at 3PM."
- Craft an effective prompt: "Based on the context below, is a follow-up meeting scheduled? If so, what date and time?"
- Run it through GPT-4 for precise interpretation.
Boom: you’ve turned a 10-hour transcript into a clean, structured answer. That’s the power of context and prompt synergy.
When You Need More Firepower: Summarizing Account Briefs
But not all tasks are that simple.
Let’s say you want a comprehensive summary of a client relationship across 12 meetings, spanning months. That’s where context engineering alone isn’t enough. You also need a model with a large context window and advanced reasoning capabilities.
This is where GPT-4, with its longer token limits and deeper chain-of-thought support, really earns its keep. And in these situations, we recommend:
- Providing a structured outline to guide the model (e.g., goals, blockers, next steps)
- Including domain-specific instructions (e.g., "Summarize the discussion from a healthcare compliance perspective")
- Running fine-tuning or real-time validation to ensure the outputs align with the desired outcome
The takeaway? For complex tasks, model quality and prompt design are both non-negotiable.
How Momentum's AI Tools Optimize for Every Use Case
At Momentum, we don’t just pick an LLM and pray. We’ve built AI tools that adapt dynamically to the problem at hand.
Our system asks:
- What is the complexity of the task?
- How much relevant context can we retrieve?
- What kind of prompt will generate the best results?
From there, we deploy an AI brain that’s perfectly aligned with the task. Sometimes it’s OpenAI’s GPT-4. Other times it’s an open source model with smart context filtering and domain-specific prompting. The point is we optimize intelligently.
Tips to Level Up Your AI Interactions
If you want better outputs from your AI systems, here’s what I recommend:
1. Use Context Engineering First: Before upgrading your model, improve the relevance of your input data. The more aligned your background info is, the better your results.
2. Iterate, Don’t Guess: Don’t assume you’ll get it right on the first try. Even the best prompt engineers use trial and error.
3. Use Chain-of-Thought Prompting: Ask the model to explain its reasoning step by step. You’ll get more transparency and better logic.
4. Leverage Templates for Consistency: Turn successful prompts into reusable formats. This is especially useful in workflows like QA, legal analysis, or customer service.
5. Monitor and Improve: Track your AI outputs, evaluate performance over time, and fine-tune both your inputs and your model choice.
AI With Purpose
Artificial intelligence isn’t magic. It’s math, semantics, and systems thinking. With so many AI models and tools now available, real success comes from combining foundational strength with great design.
At Momentum, we’ve learned that the most effective AI systems aren’t always the most expensive. They’re the ones built with care, from the ground up, with context, prompts, and performance in mind.
So next time you sit down to solve a problem with generative AI, don’t just think about what model to use.
Think about what brain you want to build.
Want to see this in action? Book a demo with our team or explore how Momentum’s AI tooling can streamline your workflows.