Several companies offer LLM access through different interfaces and pricing models. Understanding the landscape helps you choose the right tool for your needs.
1Major LLM providers
The main players in the LLM space:
**OpenAI**:\n- Models: GPT-3.5, GPT-4, GPT-4 Turbo\n- Strengths: Fast, reliable, excellent general capabilities\n- Pricing: Pay-per-use, free tier available\n- Interface: ChatGPT web/app, API
**Anthropic**:\n- Models: Claude 3 family (Haiku, Sonnet, Opus)\n- Strengths: Analysis, long contexts, safety-focused\n- Pricing: Competitive, generous free tier\n- Interface: claude.ai, API
**Google**:\n- Models: Gemini, LaMDA, PaLM\n- Strengths: Multimodal, real-time search integration\n- Pricing: Competitive, free tier available\n- Interface: gemini.google.com, API
**Meta**:\n- Models: Llama series\n- Strengths: Open-source, customizable\n- Pricing: Free for research, various commercial options\n- Interface: Various third-party implementations
2Pricing models
How LLM providers charge:
**Free tier**: Limited usage, good for learning\n- ChatGPT: 3-5 messages per hour on GPT-4\n- Claude: Generous daily limits\n- Gemini: Unlimited for basic use
**Pay-per-use**: Cost based on tokens processed\n- Input tokens (your prompt) and output tokens (response)\n- GPT-4: ~$0.03 per 1K tokens\n- Cheaper for high volume users
**Subscription**: Monthly fee for higher limits\n- ChatGPT Plus: $20/month for GPT-4 priority\n- Claude Pro: Higher usage limits
For this course, the free tiers are more than sufficient.
3API vs consumer interfaces
Two ways to access LLMs:
**Consumer interfaces** (what you'll use):\n- Web apps like ChatGPT, Claude.ai\n- Mobile apps\n- User-friendly, conversational\n- Rate limits but easy to use
**APIs** (for developers/production):\n- Direct HTTP requests\n- Programmatic access\n- More control over parameters\n- Better for building applications
This course focuses on consumer interfaces because they're accessible to everyone and teach the same prompt engineering principles.
4Future of LLM access
The LLM landscape is evolving:
- **More competition**: New providers entering regularly\n- **Better free tiers**: Increased accessibility\n- **Specialized models**: Domain-specific LLMs\n- **Open source options**: More local, private deployment\n- **Integration**: LLMs built into existing tools
The prompt engineering skills you learn now will transfer to whatever comes next. The fundamentals don't change even as the models improve.
Key Takeaways
Start with whichever provider feels most comfortable. All major LLMs can teach you prompt engineering. Free tiers from OpenAI, Anthropic, and Google give you everything you need for this course.
Try These Prompts
Put these prompt engineering concepts into practice with our beginner-friendly prompts:
Fix Common Issues
Having trouble with your prompts? These common issues and their solutions will help:
Continue Learning
Frequently Asked Questions
Do I need programming experience to learn prompt engineering?
No, prompt engineering is accessible to everyone. While some advanced techniques require understanding AI concepts, you can start creating effective prompts with just basic writing skills. This course is designed for beginners and builds up gradually.
Which AI tool should I start with?
We recommend starting with ChatGPT (free tier available) or Claude (generous free tier). Both are excellent for learning prompt engineering fundamentals. You can try Gemini later once you understand the basics. The techniques you learn work across all major AI platforms.
How long does it take to become good at prompt engineering?
Most people see significant improvements within 1-2 weeks of consistent practice. The basics can be learned quickly, but mastery comes from experimentation and iteration. Focus on understanding why techniques work rather than memorizing templates.
Can I use these techniques for work?
Absolutely! Prompt engineering is becoming an essential skill across many industries. Companies are hiring prompt engineers, and effective prompting can significantly boost productivity in content creation, analysis, coding, and many other fields.
What if the AI gives me unexpected results?
Unexpected results are part of the learning process! When this happens, analyze what went wrong: Was your instruction unclear? Did you provide enough context? Did you give good examples? Each iteration teaches you something new about how AI interprets your prompts.