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Before committing to expensive API-based models, benchmark open-source alternatives like Llama 4, Qwen3, or Mistral on your specific task. For many classification, extraction, and summarization workloads, fine-tuned open models match or beat frontier APIs at a fraction of the cost.

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

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

Challenge: Find a complex technical article (e.g., from arXiv). Ask an LLM to summarize it for three different audiences: a 10-year-old child, a university undergraduate, and a PhD-level expert in the field. Compare the differences in language, detail, and focus.

Challenge: Provide an LLM with a small dataset in a CSV or markdown table format (e.g., sales data with columns: Date, Product, Amount). Ask it to provide a summary of key trends, identify the best-selling product, and calculate the total revenue.

When asking for a summary, specify the goal. Do you need key takeaways, an abstract, a summary for a specific audience, or a certain length? This helps the LLM produce a more useful summary.

For long texts, ask the LLM to summarize section by section, then summarize the summaries. This can be more effective than a single-shot summary of a very long document.

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.

In chat applications, analyze how much of the conversation history is repeated tokens. Strategies like summarization can reduce this significantly.

Provide two or more texts to an LLM and ask it to generate a summary that highlights the key differences and similarities between them.

Understand the difference. Extractive summaries use exact sentences from the source. Abstractive summaries generate new sentences. Specify which you prefer, or let the LLM decide if it's good at both.

Ask an LLM to create comprehensive study guides from textbooks, lecture notes, or research papers. Include key concepts, definitions, and practice questions.

Use LLMs to summarize academic papers, extract key findings, and identify research gaps or future directions in specific fields.

Provide meeting transcripts to LLMs and ask them to generate structured meeting minutes with action items, decisions, and key discussion points.