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Consider using shorter models for simple tasks to save on API costs while maintaining quality.

Monitor your token usage patterns to identify opportunities for prompt optimization.

Use temperature settings wisely - lower values for factual content, higher for creative tasks.

Test prompts with different models to find the best balance of cost and performance.

Use system prompts to set context for LLMs instead of including context in every message.

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.

Smaller specialized models often outperform larger ones on domain-specific tasks.

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

Mistral's models offer an excellent balance of performance and cost for open-weight deployments.

For summarization tasks, shorter context windows help LLMs focus on key points.

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

Consider the ethical implications and potential biases of the LLM you choose for your application.

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.

Use few-shot examples in your prompts to dramatically improve output quality for specific formats.

Llama 4 models with MoE architecture offer impressive performance for self-hosted deployments.

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.""