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OpenAI's GPT-5.4 supports a massive 256K token context window, allowing you to feed entire codebases, books, or lengthy research papers into a single prompt. This makes it ideal for tasks that require long-range comprehension.

Google's Gemini 3.1 Pro pushes context limits further with a 2 million token window - roughly 1.5 million words. This allows analysis of entire software repositories, legal document sets, or multi-book corpora in one call.

Meta's Llama 4 Scout model supports a record-breaking 10 million token context window. While this is impressive for research use cases, production inference at this context depth requires significant GPU memory and specialized hardware.

Anthropic and Google now support prompt caching, where large repeated system prompts or document contexts are cached server-side. Subsequent calls with the same cached prefix cost as little as 10% of normal input price. For RAG pipelines reusing the same document chunks, this is transformative.

Use system messages effectively to set context and reduce the need for repeated instructions.

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

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

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

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

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

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

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

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.

Most large language models are trained using self-supervised learning. This means they learn from vast amounts of unlabeled text data by creating their own labels. A common technique is 'masked language modeling,' where the model learns to predict a hidden (masked) word in a sentence based on its surrounding context.

The classic 'Trolley Problem' is a frequently discussed ethical dilemma in the context of autonomous vehicles. It forces developers and ethicists to consider how an AI should be programmed to make life-or-death decisions in unavoidable accident scenarios, highlighting the complex challenge of embedding moral values into machines.

An LLM's context window is the amount of text (measured in tokens) it can consider at one time. Larger context windows allow for more complex tasks and longer conversations, but can also increase costs.

Different LLMs use different tokenizers. The same piece of text can result in a varying number of tokens depending on the model, which impacts cost and context window usage. Tools like TokenCalculator help you see these differences.

When asking for code, provide surrounding code snippets or describe the existing architecture. This gives the LLM context, leading to more compatible and accurate code generation.

When using LLM-generated content publicly, consider attributing it as AI-assisted or AI-generated, especially in contexts where transparency is important (e.g., news, academic writing).

For tasks requiring up-to-date or domain-specific knowledge, use RAG. Retrieve relevant information from a knowledge base first, then feed it to the LLM as context to generate the answer.

Use delimiters like triple backticks (```), XML tags (<tag></tag>), or quotes ("") to clearly separate different parts of your prompt, such as instructions, context, examples, and input data.

For intricate tasks, consider a structured prompt with sections like: ROLE, CONTEXT, TASK, OUTPUT_FORMAT, EXAMPLES. This organization helps the LLM understand requirements better.

To reduce hallucinations, explicitly instruct the LLM to say 'I don't know' or a similar phrase if it cannot answer a question confidently or accurately based on the provided context.

When processing long documents, try to place key instructions or questions at the beginning or end of the input text. Models sometimes pay more attention to the start/end of context.

Understand the context window limit of the model you are using. Information outside this window will be ignored. For long interactions, summarization or RAG is key.

Provide context about characters and a situation, and ask the LLM to write dialogue between them. Specify tone and subtext if needed.

Structure prompts by defining: WHAT you want the LLM to do, WHY it's important (context), and HOW it should do it (format, style, constraints).

Use LLMs as conversation partners for language learning. Practice dialogues, get grammar corrections, and learn cultural context for different languages.

When dealing with sensitive topics, instruct the LLM to consider emotional context: 'Respond with empathy and understanding, considering the emotional state of someone facing this situation.'

Different LLMs tokenize text differently. The same sentence can result in varying token counts across models, directly affecting API costs and context window usage.