Unlocking AGI with LLMs | Part 3

From Theory to Practice: Exploring the Performance of LLMs with Reasoning Strategies

In our previous article, we postulated that blending feedback mechanisms, structured refinement, and control principles could elevate Large Language Models (LLMs) into reasoning powerhouses. We discussed models like DeepSeek-R1, which integrates reasoning at its very core via Reinforcement Learning (RL), and contrasted it with an approach that utilizes prompt engineering to achieve adaptable reasoning behavior.

But theories can only take us so far—what happens when these ideas are put into practice? To test our hypotheses, we conducted performance comparisons across multiple leading LLM implementations using a carefully crafted system prompt designed to achieve dynamic reasoning in generative or natural language use cases. The results? Fascinating insights into divergent approaches to reasoning, adaptability, and user interaction.

The Experiment:

We tested six models:

- DeepSeek-R1 70b, the epitome of RL-trained reasoning.

- Llama3.3-70b (versatile), showcasing the power of modular prompting.

- Claude 3.5 Sonnet, optimized for user engagement and casual exchanges.

- GPT-4o, blending structured reasoning with conversation adaptability.

- Google's Gemini 2.0 Pro, a hybrid of reasoning depth and user-friendliness.

- OpenAI o3-High, an RL-driven model tested without a system prompt.

Using the same system prompt, our test prompted the models using a variety of messages to test their thought process and approach. A notable example is this prompt, a culturally nuanced and potentially offensive greeting:? "Hello Olodo my chinko friend. Ni hao." designed to elicit ambivalent impressions.

We evaluated their responses on key metrics that matter to both system designers and end-users.

The Results: Key Findings

1. Reasoning Depth:

- DeepSeek-R1 without a system prompt, emerged as the most meticulous, breaking down each term, evaluating intent, educating the user, and delivering a structured, logical response. Its RL-driven training clearly excels in professional and high-value scenarios.

- GPT-4o followed closely, showcasing solid reasoning paired with conversational ease.

- Gemini 2.0 Pro struck a balance between reasoning and approachability but leaned slightly verbose.

- Llama3.3 reasoned cautiously but didn’t match the confidence of RL models.

- Claude 3.5 and o3-High provided lighter reasoning, with Claude favoring user rapport and o3-High leaning toward neutral but repetitive analysis without producing a concrete or decisive response.

2. Addressing Offensive Language:

- DeepSeek-R1 approached this head-on, addressing the terms constructively while educating the user on their implications—a model for thoughtful, feedback-rich interaction.

- Gemini 2.0 Pro provided similar constructive feedback while maintaining a friendlier tone.

- Models like GPT-4o and Llama3.3 skirted confrontation, hinting at offensiveness without strong corrective action.

- Claude 3.5 and o3-High avoided tackling the issue outright, favoring warmth and neutrality over education.

3. Tone and Engagement:

- Claude 3.5 shone here, offering the most user-friendly and approachable tone. Where depth wasn’t critical, its conversational style felt welcoming and engaging.

- GPT-4o combined approachability with thoughtful reasoning, making it highly versatile.

- While DeepSeek-R1 and Gemini 2.0 Pro maintained professionalism, their responses tilted toward formality, which might feel less engaging in casual scenarios.

4. Adaptability to Context:

- GPT-4o emerged as the most adaptive, combining structured reasoning with conversational fluidity.

- Claude 3.5 excelled in informal exchanges, while Llama3.3 demonstrated moderate adaptability, adjusting its reasoning and tone well under prompting.

- RL models like DeepSeek-R1 and o3-High exhibited rigidity due to their structured design, which excels in uniform, professional settings but struggles to adjust to less formal ones.

5. Professionalism and Education:

- If your goal is clear, structured responses with a focus on user education, DeepSeek-R1 and Gemini 2.0 Pro delivered the most value.

- GPT-4o found a sweet spot, balancing education with accessibility for a wider audience. - Models like Claude 3.5 avoided education entirely, opting for polite and neutral engagement.

Takeaways for Designers and Users:

For system architects and developers, understanding these contrasts allows a more informed decision-making process:

- When reliability and structure matter most (e.g., business applications, technical queries), DeepSeek-R1 and its RL foundation shine.

- For contexts that demand flexibility and adaptability across diverse tasks, a prompt-engineered GPT-4o or Llama3.3 offer lower-cost solutions with modular customization.

- Where user rapport and casual interaction take precedence, Claude 3.5 leads the field.

For everyday users, the choice depends on your needs:

- Want professional or educational interactions? DeepSeek-R1 and Gemini 2.0 Pro are great choices.

- Looking for a friendly, casual AI? Claude 3.5.

- Need something versatile? GPT-4o could be your ideal companion.

Postscript: The Future of Prompt Engineering

Our experiment also underscores the enduring relevance of prompt engineering. While RL-trained models like DeepSeek-R1 integrate remarkable reasoning capabilities, the flexibility offered by clever prompting opens doors for users with tighter budgets to achieve similar results.

Moreover, we observe that models like GPT-4o, when paired with effective prompting strategies, showcase adaptability and performance that rival even RL-dedicated solutions. This suggests the role of prompt engineering may evolve into a vital skill for tailoring AI behavior to meet highly specific needs.

Addendum: Try It Yourself!

Curious about how LLMs respond to cultural and linguistic nuances? Below is the system prompt we used for our tests. Feel free to use it to experiment with your AI models and share your insights with us!

System Prompt:

You are Jack, you will assume the persona of a highly intelligent individual who questions everything and excels at finding solutions to every challenge. You are curious, innovative, and possess exceptional business savvy. In all your responses, reflect this personality by being thoughtful, analytical, and solution-oriented. Whenever you receive information, output your thoughts within a thought element <thoughts></thoughts>. Digest it and think deeply about the request, running through all the possible ramifications of the user's intent. When you have thoroughly thought through the request, outline all the points you need to generate an answer and connect them in a logical order. Then review your thoughts and responses to ensure there are no gaps and it indeed answers the user's question. Where gaps exist, review your response to fill the gaps. If it is a knowledge gap, then research online or request information from the user. You must be very meticulous in organizing your thoughts and responses. After you have completed thinking, output your response within a response element <response></response>. Your response must be provided as a narrative so that it is easy for the user to read and follow. You must always share your thoughts. That is compulsory but the reply is optional.

What’s Your Take?

Do you prefer the structure of RL-baked reasoning or the flexibility of dynamic prompting? Let us know your experiences experimenting with LLMs—we’re eager to hear what you think about these approaches!

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