2 min read Large Language Models

Things I Wish I Knew Sooner About Using AI - LLM

Things I Wish I Knew Sooner About Using AI - LLM

Maximizing the Potential of Large Language Models (LLMs)

Introduction

In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) like ChatGPT have emerged as powerful tools capable of transforming everything from daily tasks to complex projects. ChatGPT, powered by OpenAI’s GPT-4, is a prominent example—but it’s far from the only option. Other major players include Claude by Anthropic and Gemini by Google DeepMind, each offering distinct capabilities and design philosophies.

The purpose of this article is to highlight the broader potential of LLMs beyond ChatGPT and to offer insights into how you can maximize their capabilities—including running your own model, like Gemma, directly on your laptop.

Understanding LLMs

Before diving into advanced techniques, it's essential to understand what defines a large language model. At its core, an LLM is a neural network trained on massive amounts of text data, allowing it to generate intelligent, context-aware responses across a wide range of topics.

While models like ChatGPT, Claude, and Gemini each have their own strengths, running your own LLM—such as Gemma—provides unique advantages. These include reduced reliance on cloud-based services, enhanced data privacy, and potential cost savings over time. The diversity of available models underscores the value of exploring different tools to find the best fit for your needs.

The Power of Collaboration

A common misconception is that LLMs function only as advanced chatbots. In reality, treating them as collaborative partners can unlock far greater potential.

By interacting with an LLM in an iterative, contextual way, users can shape it to support their specific workflows and goals. For example, you can instruct your model to:

  • Avoid filler or "fluff"
  • Use formal or informal tones
  • Prioritize brevity or thoroughness
  • Follow particular formatting conventions

This approach transforms the LLM into a flexible assistant tailored to your preferences—more like a thought partner than a tool.

Effective Prompting Techniques

To fully harness the power of any LLM—whether ChatGPT, Claude, Gemini, or Gemma—use the following structured techniques:

  • Role Definition: Assign the AI a specific identity (e.g., historian, copywriter, analyst).
  • Contextual Analysis: Provide background information or source material to guide responses.
  • Multi-Perspective Queries: Ask for insights from different viewpoints within a single prompt.
  • Practical Applications: Use the AI for real-world tasks like email drafting, content writing, or strategic planning.
  • Response Management: Refine your prompts to keep the AI focused and aligned with your intent.
  • Example-Based Grounding: Use real or hypothetical examples to make prompts more relatable and concrete.
  • Progressive Prompting: Start simple, then increase complexity to build more nuanced responses.
  • Clarity and Structure: Clearly define your desired output format to improve precision and relevance.

These methods help turn a general-purpose tool into a powerful, purpose-driven assistant.

Exploring Different LLMs

While ChatGPT remains a popular choice, exploring alternative LLMs can bring added value depending on your needs:

  • Claude (Anthropic): Known for its long-context reasoning and commitment to safety.
  • Gemini (Google DeepMind): Excels in multimodal tasks and deep integration with Google services.
  • Gemma (open-source): Offers offline control, local customization, and full data privacy for tech-savvy users.

Each of these models brings unique strengths to the table. Experimenting with different LLMs enables you to find the best match for specific tasks, whether it's creative writing, research synthesis, coding support, or private local use.