Unlocking AGI with LLMs | Part 2

What’s Smarter: Baked Intelligence or Modular Flexibility?

In our last article, we explored how blending feedback loops, control engineering principles, and thoughtful methodologies could turbocharge existing LLMs—pushing us closer to true AI generalization. Since then, we’ve dived deeper, comparing two intriguing approaches from vastly different angles:

DeepSeek-R1, a state-of-the-art reasoning powerhouse, and our own?prompt-powered architecture.

Here’s what we found—and how this debate might shape the future for system architects, developers, and even everyday users.

DeepSeek’s Approach: Intelligence Baked In

DeepSeek-R1 uses reinforcement learning (RL) to train its reasoning capabilities directly?into?the model. It’s like a supercharged System-on-a-Chip (SoC), designed to handle reasoning, logic, and problem-solving natively. By omitting system prompts and instead relying on highly tuned configurations (e.g.,?<think>?tags and temperature control), DeepSeek provides a polished, plug-and-play reasoning experience.

Pros for Systems Architects & Developers: -?Streamlined Operations: No need for personas or complex prompt engineering—reasoning is baked in. -?Consistent Outputs: Through RL and reward models, DeepSeek delivers dependable reasoning patterns, crucial for high-stakes scenarios like coding, math, or science. -?Minimal Expertise Required: Architects can focus on integrating DeepSeek without extensive prompt optimization.

Cons: -?Inflexibility: Once trained, modifying behavior requires additional (and costly) retraining. -?Cost Burden: Training using RL is expensive, with significant computational and resource overhead. -?Limited Adaptability: Users needing custom responses or adaptive behaviors must play within DeepSeek’s boundaries.

Prompt Engineering: The Modular Mindset

Instead of baking reasoning into the LLM, we treated it as a microprocessor, building modular components around it. Using sophisticated prompting techniques—like our?Jack persona—we encouraged even "vanilla" LLMs, not built for reasoning, to think, analyze, and respond like experts.

Think of it like crafting an audio amplifier: while DeepSeek is the complete package (like the LM3886 chip), our approach builds on an op-amp (standard LLM) with discrete components (feedback loops, system prompts).

Pros for Systems Architects & Developers: -?Flexibility: Behaviors and processes can be modified instantly by tweaking prompts, no retraining needed. -?Cost-Effective: Prompting is exponentially cheaper than training—simply re-engineer the dialogue without burning GPUs for weeks. -?Adaptability: Custom solutions can be achieved almost on the fly, adapting the same model for diverse tasks, personas, and business needs.

Cons: -?Complexity: Prompt engineering is an art that requires time, intuition, and a strong grasp of model behavior. -?Inconsistency: Without training, responses can sometimes lack the polish and coherence of an RL-tuned model. -?Performance Floor: Without true reasoning baked in, hitting the performance ceiling of models like DeepSeek may require additional layers of pre- and post-processing.

The Takeaway for System Architects vs. End Users

For?system architects?or?developers, the choice comes down to control versus convenience. If flexibility and cost-efficiency are top priorities,?prompting?will be your best friend. It allows for rapid iteration, adaptability, and low barriers to experimentation. Every tweak is a new opportunity.

However, for users or teams looking to deploy a?vanilla product?quickly with minimal overhead,?DeepSeek’s RL-driven reasoning?offers a polished, streamlined solution. It has the out-of-the-box intelligence ready to handle complex reasoning straight away.

The Elephant in the Room: Training Cost

Training a DeepSeek-like model isn’t just a technical challenge—it’s a financial one. Running RL at scale requires massive computational investment, making these solutions accessible mostly to organizations with considerable budgets.

Prompt engineering, on the other hand, democratizes AI by putting the tools of customization in everyone’s hands. With clever guidance and the right principles, virtually anyone can tailor existing LLMs into highly competent, domain-specific problem-solvers.

The Future: Will Prompt Engineering Stay Relevant?

The rise of models like DeepSeek raises an important question: will prompt engineering become obsolete? Not likely.

Instead, prompt engineering might take on a new role,?bridging the gaps?between off-the-shelf powerful models and the unique needs of individual businesses, researchers, and users. It remains the most cost-effective method to align AI with human-like reasoning?without breaking the bank.

As for us, we remain endlessly fascinated by the possibilities of both approaches. So, what about you—would you rather bake intelligence in or build around it??Tell us your thoughts below!

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