GPT-6.1 Sol: 10 Prompts That Will Save Your Lot of Time

https://bitbiased.ai/ai-automation-services GPT-6.1 Sol is supposed to make roughly 32% fewer factual mistakes than GPT-6 Sol on hard questions, while costing about one-fifth as much per token as GPT-6 Astra. But benchmark numbers don’t tell you what happens when you actually sit down and use it. So this video skips the benchmark charts and tests GPT-6.1 Sol with the kinds of prompts normal people actually type: learning a difficult topic, rewriting an email, researching current products, analyzing PDFs and spreadsheets, building and debugging code, and making everyday decisions. According to OpenAI’s own figures, Sol’s error rate on hard factual questions drops from 11.4% with GPT-6 Sol to 7.7%. It costs $2 per million input tokens compared with Astra’s $10, while OpenAI says it gets surprisingly close to Astra on coding and document work. Sol also has a context window of roughly 1.05 million tokens, making long documents and complex projects one of its most interesting use cases. Its knowledge cutoff is April 30, 2026, however, so anything newer still requires web search. But the bigger lesson here isn’t about specifications. It’s about how little prompting you actually need. For learning, we start with a difficult topic and simply ask Sol to explain it in plain language using one everyday analogy. Then we increase the difficulty without restarting the conversation. The same approach turns Sol into a basic tutor for Python: explain the concept, give me an exercise, and don’t reveal the solution until I ask. For writing, we test whether Sol can take an ordinary email and make it professional without making it sound robotic. Tiny follow-ups like “make it shorter” and “make it less formal” quickly produce completely different versions without requiring elaborate prompts. Research is where things get more serious. Sol compares current AI video generators using web search, but instead of simply trusting the answer, we ask it to show the sources behind its main claims and identify the information it’s least confident about. That extra step matters when prices, features, and product details can change quickly. Then we move into files. A real PDF gets turned into 10 bullet points and then condensed again into five key takeaways. A spreadsheet gets analyzed using normal questions like “Which category has the highest total spending?” before Sol calculates totals and averages by category. For coding, we go from one sentence — “Create a simple expense calculator in HTML” — to a working tool. Then we add dark mode and CSV downloads with another short instruction. We also give Sol broken Python code and ask it to find the problem, fix it, and explain the mistake simply. Finally, there’s one prompting technique that works almost everywhere: make Sol interview you first. Instead of dumping every detail into a giant prompt, ask it to question you about your needs before recommending something. We test that approach with choosing between a laptop and desktop, creating a meal plan, and planning a weekend getaway. Sol still needs supervision. OpenAI’s own testing shows it can make factual mistakes, current information requires web search, and code, calculations, and important claims should still be checked. But you don’t need complicated “prompt engineering” to get substantially better answers. Seven tiny follow-ups handle most of the work: make it shorter, explain it simply, give me an example, put this in a table, check your answer, show me your sources, and ask me questions first. CHAPTERS 00:00 GPT-6.1 Sol With Normal Prompts 01:28 What Sol Is, and Who It's For 02:27 Learning: A Hard Topic at Exactly Your Level 04:06 Writing: Emails and Paragraphs That Sound Right 05:30 Research: Comparisons You Can Actually Check 07:23 Files: A PDF and a Spreadsheet 09:52 Coding: Build a Small Tool, Then Fix a Broken One 11:24 Everyday Decisions: Make Sol Interview You First 12:30 Where Sol Still Needs You 13:00 Seven Tiny Follow-Ups That Fix Almost Everything #gpt61 #gpt61sol #chatgpt #openai #ai