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Breaking complex prompt tasks into subtasks can improve accuracy and reduce hallucinations.

For code generation, ask the model to explain its logic after generating the code.

Test models with smaller context windows first to save on API costs during development.

For the most cost-effective solution, GPT-4o-mini provides great quality at a fraction of GPT-4o's cost.

Regularly check model provider documentation for updated tokenization rules and pricing.

Experiment with different temperature settings to control the creativity versus predictability of model outputs.

Keep your prompts concise but informative to maximize the value of each token.

Use Claude Code (CLI) for agentic coding tasks — it excels at multi-file refactors and complex changes.

Gemini 2.5 Flash offers near-Pro quality at a fraction of the price — ideal for high-volume use cases.

Chain-of-thought prompting (asking the model to 'think step by step') dramatically improves reasoning accuracy.

Use cached input pricing when available — repeated system prompts can save 50%+ on input costs.

For coding tasks, include relevant file context and error messages to get much better responses.

DeepSeek R1 offers strong reasoning capabilities at very competitive pricing for budget-conscious projects.

When comparing models, test with YOUR actual use case — benchmarks don't always match real-world performance.

Use structured output (JSON mode) when you need parseable responses from LLMs.

Batch API calls when possible — most providers offer significant discounts for batch processing.

For long conversations, periodically summarize the context to stay within token limits efficiently.

Always validate AI-generated code before deploying — even the best models can introduce subtle bugs.

Use model routing: send simple tasks to cheap/fast models and complex ones to premium models.

Anthropic's Claude excels at following complex, multi-step instructions with high fidelity.

Consider using open-source models for internal tools where data privacy is a concern.

The 'lost in the middle' problem is real — put important context at the start or end of long prompts.

Use streaming responses for better UX — users perceive faster models as more capable.

For RAG applications, chunk your documents into 512-1024 token segments for optimal retrieval.

Grok 3 offers real-time web access — useful for tasks requiring current information.

When building agents, use tool-use/function calling instead of parsing free text for reliability.

Track your AI spend across providers — costs can escalate quickly with agentic workflows.

Use Gemini's 1M+ token context window for processing entire codebases or long documents in one shot.

For creative writing, increase temperature (0.7-1.0) and for factual tasks, keep it low (0.0-0.3).

OpenAI's o-series models (o3, o4-mini) use 'thinking tokens' that count toward your bill — factor this in.

Use model-specific prompting techniques: Claude likes XML, GPT likes markdown, Gemini handles multimodal well.

""It's very hard to get a machine to pop out of the system that it's in. We are constantly popping out of the system.""

The datasets used to train frontier large language models are massive, often consisting of trillions of tokens. For example, the dataset for Llama 3 was over 15 trillion tokens, sourced from a vast collection of publicly available text and code from the internet.

Never blindly trust the output of an LLM, especially for factual information or code. Always have a validation step, whether it's manual review, fact-checking against reliable sources, or running generated code in a sandboxed environment. LLMs can and do make mistakes.

""AI is not going to be a single technology. It's a platform that will be applied to every industry.""

Challenge: Ask an LLM to write a poem (e.g., a sonnet or a haiku) about a specific topic, but add unusual constraints. For example, 'Write a sonnet about a traffic jam, but do not use the letter E.'

For complex tasks, don't try to write the perfect prompt in one go. Start with a simple instruction, see the result, and then iteratively add more constraints, context, and examples to refine the output until it meets your requirements. This is more effective than starting with an overly complex prompt.

In-context learning is the ability of a large language model to learn a new task at inference time simply by being provided with a few examples in the prompt (few-shot prompting), without any updates to the model's weights. This is a powerful, emergent property of scale.

Challenge: Present an LLM with a classic ethical dilemma (e.g., the Trolley Problem or a scenario involving privacy vs. security). Ask it to argue for both sides of the dilemma, using different ethical frameworks (like utilitarianism and deontology).

""A neural network is a guesser, a pattern-matcher, a storyteller. It's a carnival mirror, reflecting back the things we've shown it, only warped.""

Most modern LLMs use subword tokenization (e.g., BPE, WordPiece). This allows the model to handle rare or unknown words by breaking them down into smaller, known subwords. It strikes a balance between the simplicity of word-level tokenization and the flexibility of character-level tokenization, allowing for a manageable vocabulary size while still being able to represent any word.

""If we can solve intelligence, then we can use that to help us solve everything else.""

Challenge: Ask an LLM to invent a futuristic product. The prompt should ask for the product's name, a marketing tagline, a short description of its features, and its target audience.

Don't use a powerful, expensive model like GPT-5 or Claude 4.1 for simple tasks like reformatting text, correcting grammar, or classifying sentiment. Use a smaller, faster, and cheaper model like GPT-4o-mini or a fine-tuned open-source model. This is a key principle of cost-effective AI development.