ChatGPT Basics: Everything You Need to Know

00:00 OpenI just released their newest lineup GPT 5.6, three levels of models and an agent called ChatgPT work that literally does the whole job for you. I'll show you which model to pick for which task, how to make ChatGpt give you a finished result instead of filler, and what their new agent is actually capable of. Every prompt will be on screen. All you

00:27 have to do is copy it. Before we start prompting, there is one thing that can be confusing. In a normal chat GBT conversation, you do not select Luna or Terra as separate models. On eligible paid plans, GPT 5.6 SOL appears through the reasoning options available in regular Chad GBT. Luna and Terra become selectable in products such as CHED GBT

00:51 work. Inside work, eligible paid users can choose between Saul, Terra, and Luna. So the easiest way to think about the lineup is this. Luna is the fast lowcost option. Terra is the balanced option for everyday work. Saul is the model I would consider when the task becomes deeper, larger or more sensitive to misconnections. And work is different

01:15 from all three because work is not just about getting one answer. It is about giving Chad GBT a project and asking it to produce an actual deliverable. Let me show you why model choice should start with a task, not with the biggest model name. I gave Luna, Terra, and Saul exactly the same simple job. Rewrite a short meeting message so it sounds

01:38 polite and natural. Offer two new time options and keep it under 80 words. And in the real test, all free answers were ready to send. Luna wrote a short, polite rescheduling message with both options. Terra did the same. Saul did the same. The wording changed slightly, but the practical result was almost identical. That is the first rule I want

02:01 you to remember. Do not use a more expensive level just because it exists. For a simple task that you can verify in 10 seconds, the smallest model may already be enough. There is no price for using flagship reasoning to rewrite two sentences. One reason I like testing the same prompt across different models is that the differences are often smaller

02:24 than people expect on simple tasks. On harder tasks, those differences can matter much more. AI Master Pro gives you one place to work with leading AI models, including thinking models, without rebuilding the same workflow across a pile of separate subscriptions. You can run the same prompt, compare the outputs, keep your files and context together, and decide which model

02:49 actually fits the job. For me, the bigger use case is content production. Inside AM Master Pro, you can use AI agents to help run your own channel. You remain in control of the channel and the final decisions. The agents can help with planning, preparing speech ready scripts, and developing thumbnail concepts. The agent crew is continuing to roll out feature by feature. The idea

03:12 is not that someone else runs your channel for you. You use the agents yourself to remove repetitive work from your process. There is also a full academy inside the platform. It includes more than 200 lessons and almost 30 hours of training. The learning path is designed to take someone from beginner level AI workflows into more advanced

03:35 content and production systems. There are also published user results and testimonials. One creator in the material I reviewed said they had followed AI Master for months before trying the pro course and that the training helped them get their small agency moving. There is also a community of more than 12,000 users. So, the platform combines the tools, training, [music] and an active user base in one

03:59 place. If you want to try it, use the link in the description. Click buy, choose the annual plan, and enter the promo code from this video. After checkout, you receive an email, open it, sign into your account, and you can start using the platform. The purchase also includes a 7-day money back guarantee, so you have time to test whether the workflow fits you. Now,

04:21 let's go back to GBT 5.6. six because the next two tests are where model choice becomes more interesting. For the second test, I uploaded a messy page of meeting notes. Some lines were confirmed decisions, some were open questions. One note said the product launch might move to next Tuesday, while another said the launch was still scheduled for Friday. I

04:45 asked Luna and Terara to turn the notes into a follow-up email, separate confirmed decisions from unresolved items, and avoid inventing owners, deadlines, or decisions. This results surprised me a little. Both Luna and Terra caught the Friday versus Tuesday contradiction. Both kept the launch date unresolved instead of quietly choosing one version. Both avoided turning an

05:10 unconfirmed suggestion about Marcus into a confirmed assignment. And Luna was actually slightly cleaner in one place. It kept Daniel's product readiness confirmation under next actions while Terra placed that line under confirmed decisions. That does not mean Luna is now the best model for every office task. It means something more useful. The boundary between model levels is not

05:34 a neat line where one model suddenly becomes intelligent and the other becomes useless. Sometimes the smaller model will handle a surprisingly structured task just fine. That is why I would not upgrade based on the label alone. I would upgrade when the cost of a missed relationship becomes higher or when the amount of material becomes

05:56 harder to inspect manually. So let's push the task further. If you're running a business and juggling 10 different tools right now, what if I told you that one platform could replace all of them? This is go high level. You get access to pretty much all the features. Social media planner. You can schedule posts, see your content calendar, and even repost content across multiple platforms with one click. Email marketing. Go

06:20 Highle has a full email suite. You can create campaigns, build email templates with a drag and drop editor. Go HighLevel has a built-in calendar and booking system. This replaces tools like Calendarly. You can create different calendar types, one-on-one meetings, group calls, roundrobin scheduling if you have a team. And here's the best part. Go HighLevel has a ton of

06:43 pre-built workflow templates. I've linked a 30-day free trial in the description, not the standard 14 days, an exclusive extended trial. For the third test, I uploaded three files: an old rental agreement, a new rental agreement, and a separate fee schedule referenced by the new contract. The key detail was hidden across documents. The new agreement increased the base rent,

07:07 but it also pointed to schedule B for a mandatory recurring charge. So, the model had to follow the cross document reference before it could calculate the real firstear difference. Here, Terra got the core answer right. It followed the reference into schedule B, found the mandatory 45 monthly administrative charge, and calculated the correct firstear increase of €1,140.

07:31 It also caught the changes to late payment terms, renewal notice, early termination notice, and the new early termination charge. Then I ran the same task on Saul. Saul reached the same bottom line calculation, but the review was more exhaustive. It separated more of the secondary fees, documented more cross-file dependencies, and even flagged a document matching detail that

07:55 could not be independently verified from the supplied files. So, this is the distinction I would actually use. Terra was already enough to make the main financial comparison correctly. Saul became useful when I wanted the deepest possible audit of the material, including edge cases and uncertainties that were not necessary for the headline answer. That gives us a much better

08:18 model selection rule than always use the biggest model. Use the smallest level that reliably gives you the result you need. Move up when the test contains more connected information, when the cost of a missed detail is higher, or when you want a more exhaustive review than you can comfortably inspect yourself. Model choice matters, but prompting matters more often than people

08:42 think. Most beginners still use Chad GBT like a search box. They type something like this. And here is the important part. The answer can look good. In my test, Chad GBT produced a clean 7-day plan with themes like deep work, errands, exercise, meal preparation, social time, and weekly planning. It

09:04 even acknowledged that it could not access my calendar. The problem was not that the answer looked bad. The problem was that almost the entire plan was built from assumptions. I never told it my working hours. I never told it about my commute. I never told it that I had a doctor's appointment. I never told it that I needed free workouts, two course lessons, groceries on Saturday morning,

09:28 or a completely free Saturday evening. So, the response was polished, but it was not actually my week. That is the difference between a plausible answer and a useful answer. For tasks that matter, I use six checkpoints. Ro, task, context, constraints, format, and model choice. The model choice happens before I send the prompt. It is not a magic

09:52 sentence I paste at the end. Let's rebuild the same weekly plan request properly. The new answer was much more specific. It placed the commute around work. It scheduled the three workouts on non-consecutive days. It placed both course lessons. It kept Saturday evening free. It built the doctor's appointment into Wednesday instead of pretending that every weekday was identical. But

10:15 the test also revealed something important. The model still made two assumptions. It estimated that the doctor's appointment would last 1 hour even though I never provided a duration. And it assumed that the 25-minute trip to the clinic started from my workplace. That is why a strong prompt is not a guarantee of perfection. A strong prompt gives you a better answer and clearer

10:40 criteria for checking it. Now let's break down the six checkpoints. The first is raw. I said you are a practical personal planner. The word practical is doing more work than a fake biography about 20 years of productivity experience. I am telling the model what kind of judgment I want. For a resume, a recruiter perspective can be useful. For

11:03 a study plan, a tutor focused on retention can be useful. For a product review, a skeptical buyer can be useful. Use a role when the perspective changes what a good answer looks like. The second checkpoint is task. Help me with my week is not a finished task. Create a 7-day schedule is help with my resume is vague. Compare my resume with this job

11:26 description and rewrite the weak sections. Is it deliverable? Whenever Chat GBT gives you a long answer that goes nowhere, ask yourself whether you actually defined what should exist when the model finishes. The third checkpoint is context. Context is information that changes the answer. My work hours changed the answer. My commute changed the answer. The doctor's appointment

11:50 changed the answer. Saturday evening being protected. Change the answer. You do not need to dump your entire life into the prompt. Ask one question. If I remove this detail, could the model make a different decision? If yes, that detail probably belongs in the context. The fourth checkpoint is constraints. Constraints are where you prevent

12:12 predictable failure. Do not schedule before a certain time. Do not exceed the budget. Do not invent missing facts. Do not change dates that are already fixed. Do not put workouts on consecutive days. A model can satisfy the task and still produce something unusable if you never define the boundaries. The fifth checkpoint is format. Think about what

12:36 you will do with the answer next. If you need to send it, ask for an email. If you need to compare options, ask for a table or ranked comparison. If you need to execute it, ask for a checklist or schedule. In this case, I ask for a day-by-day plan with times and durations because that is something I can actually follow. The sixth checkpoint is model choice. For a simple rewrite, start

12:59 small. For normal planning and document work, use the balance level when you need more reliability across several conditions. For large connected material or a deep audit, move up to the flagship. And when the job is a project rather than a single answer, move into work. Here is the reusable version of the framework. Choose the model separately before you send the prompt.

13:22 The model is part of the workflow, not a sentence that magically switches the system for you. Another beginner mistake is opening a new chat every time one thing changes. You already paid the cost of giving the model context. Use it. In the same weekly plan conversation, I added a Thursday meeting and told Chad GBT to rebuild only Thursday and Friday. That phrase only Thursday and Friday

13:47 matters. I am not asking for a new plan. I am defining the smallest allowed change. Then I can transform the same result for another use. Same context, different deliverable. That is a much better way to use a conversation than repeatedly starting from zero. There are also a few follow-up commands I save because they work across many tasks. The

14:10 first is for missing information. This is especially useful before a recommendation or plan. In our weekly example, the model had already assumed the appointment duration and the starting point for the clinic journey. Asking for missing information gives it a chance to expose those gaps instead of hiding them inside a confident schedule. The second command is for unsupported content. This does not magically

14:34 guarantee truth. It forces another pass focused on evidence. The third is for alternatives with a purpose. Notice that I am not asking for three more ideas. Each version is optimized for a different goal. The fourth is self-critique and the fifth is controlled compression. These are useful because they describe the transformation

14:56 you want. Make it better is vague. Shorten it while preserving dates, numbers, decisions, and actions is executable. Uploading a file does not automatically create a good task. I tested this with a resume. First, I uploaded only the resume and wrote the answer was detailed. It commented on structure, ATS readability, professional

15:20 direction, wording, skills, and layout, but even suggested possible ways to make Excel and SQL skills more specific. But the model had no job description. So it had no way to know what decision I actually cared about. Was I applying for marketing, operations, business analysis, administration? The answer was a general resume critique because that

15:43 was the only task I gave it. Then I uploaded the resume and a junior business analyst job description together. Now the answer changed completely. The model ranked the requirements that mattered for this specific role. It connected them to evidence that actually existed in the resume. It created a separate section called gaps that cannot honestly be covered by rewriting. It explicitly

16:07 refused to invent requirements gathering experience, process improvement work, applied SQL projects, advanced Excel or PowerBI experience that the candidate did not have. That is what I want from document analysis. Not make this document sound impressive. I want the model to connect evidence to decision. Whenever you upload a file, ask four

16:31 questions. What exactly should the model find? What decision will I make from the result? What evidence should it show me? And what is it forbidden to invent? For important documents, ask for section numbers, [music] page references, or direct quotations that gives you a path back to the source. The same principle works with screenshots. I used the mock subscription management screen and asked

16:54 Chad GBT to explain how to turn off automatic renewal without immediately deleting the account or losing [music] paid access. The most important line is not the goal. It is if the required option is not visible, do not guess. A screenshot is evidence with boundaries. Tell the model to stay inside them. Chat GBT work changes the way you should

17:17 think about prompting. In a normal chat, you are usually asking for an answer. In work, you can ask ChatGBT to create or edit actual documents, spreadsheets, presentations, reports, and analyses. That means the prompt should describe what finished looks like, not just what topic you want to discuss. I first tested work with the weakest possible

17:41 travel prompt. Work did not blindly invent a holiday. It stopped and asked me for the dates, departure city, trip length, budget, and travel preferences. That is good behavior, but it also shows the cost of a vague prompt. The agent cannot know what success means. So, it has to spend the first round extracting the project definition from me. Now,

18:04 compare that with a full outcome prompt. And this is where the real behavior was more interesting than the idealized demo. work could not verify live exact date flight and hotel quotes that were necessary to prove the trip fit inside the 1,500 budget. So, it stopped. Instead of fabricating bookable prices, work created a source feasibility

18:28 document. It preserved the information it could verify, including attraction hours, some transport and ticket [music] costs, exchange rate information, constraints, and source links. It also listed the checks that were still missing before a final booking plan could be trusted. That is a much more useful lesson than pretending an agent always finishes everything perfectly. A

18:51 good agent prompt should make it safe for the agent to stop. You want the agent to know the difference between I completed the project and I reached the limit of what I can verify. For work, I think in five parts. Outcome, what should exist at the end? sources. What information must be verified and where should the evidence be preserved? Deliverables. Do I need a document, a

19:15 spreadsheet, a comparison table, a checklist, or several files? Constraints. What cannot be violated? And verification. What must the agent recheck before it calls the work finished? Here is a reusable work template you can copy and adapt. You can reinforce those requirements with follow-up instructions. For example, if you need a real editable comparison, say

19:38 this. If currencies are mixed, define the conversion rule. If current facts matter, require accessible sources. And give the agent permission to stop when the missing information can change the result. When I added that final instruction to the Rome task, work stopped and asked only two things. First, whether a carry-on luggage only meant a full overhead cabin bag or just

20:02 a personal item. Second, whether I could provide live booking links or screenshots for the exact dates because without exact date prices and schedules, it could not verify the budget or timing constraints. That is the behavior you want from an agent, not blind confidence, a clear stopping condition. Now, let's look at structured data. I

20:25 uploaded a CSV containing 6 months of bank transactions. The file included salary, normal purchases, transfers between my own accounts, a refund, an annual fitness payment, change in subscriptions, and a possible duplicate food delivery charge. A weak construction would be analyze my spending. The problem is that the model would have to invent the accounting

20:48 rules. So, I defined them first. The result was not just a paragraph. Chad GBT calculated monthly spending, excluded salary and own account transfers, applied the refund, and flagged two identical 4280 food delivery charges as a possible duplicate without deleting either one automatically. Then it created an Excel analysis file, a

21:11 one-page PDF summary, a monthly spending line chart, and a bar chart of the five largest categories. The summary also gave three concrete savings opportunities. Reduce one average dining or takeout purchase per month. Review an unused digital subscription and check whether a 12uro bank fee is recurring and avoidable. The reusable lesson is

21:34 simple. Define the data rules before asking for the inside. What counts? What does not count? How should refunds, transfers, duplicates, and unknown transactions be handled? Only then ask for totals, charts, and recommendations. Otherwise, you can get a beautiful visualization of bad logic. Technique one is to make Chad GBT ask questions before making a recommendation. A prompt

21:59 like this is almost useless. The model does not know my budget, operating system preference, weight limit, battery requirement, or whether I edit video. So instead, I write this. Then I answer with the decision criteria. And before naming products, I ask it to turn those answers into requirements. This works for more than laptops, courses, travel,

22:22 insurance, career decisions, anything where missing context can change the recommendation. Technique two is separating facts from assumptions. I uploaded the new rental agreement and asked Chad GBT to divide the answer into confirmed facts, reasonable assumptions, and missing information. The result created visible boundaries. The rent amount and notice periods were listed as

22:47 confirmed facts with section numbers. Things like the exact end date of the 12-month term were treated as reasonable interpretations rather than confirmed text. And missing items, including the referenced schedule B were placed in a separate section. This is useful whenever uncertainty matters. contracts, research, policies, pricing, anything

23:10 where a confident assumption can quietly turn into a bad decision. Technique three is self-critique. I gave Chad GBT a small set of facts about a candidate and ask it to write a cover letter for a junior business analyst role. Then I ask it to review its own draft as a skeptical recruiter. This is not a magic accuracy button. It is a cheap second pass. The important part is that the

23:35 critique has a role, a target, and a correction step. Check your answer is weak. Review this as a skeptical recruiter. Identify three unsupported claims and rewrite only using known facts is much stronger. One final feature that beginners often ignore is personalization. In the current interface, the setting is called base style and tone. You can choose styles

23:59 such as efficient, friendly, and professional. Changing the personality changes how Chad GBT communicates. It does not change the underlying capabilities or safety rules. I tested the same salary increase prompt across those styles. The point is not that one style is smarter. The point is that you can choose the delivery that fits how you like to work. Efficient is useful

24:23 when you want concise, direct answers. Friendly is warmer and more conversational. Professional is more polished and workplace oriented. And then there are custom instructions. These are for preferences you keep repeating in conversation after conversation. For example, you put that into custom instructions inside personalization, not into every new

24:46 chat. The purpose is not to make the model more intelligent. It is to remove repeated friction from the way you use it. So let's put everything together for a short rewrite, a simple classification, a quick summary, or another task that is easy to verify. Start with the smallest model level that can do the job. Our first test showed that Luna, Terra, and Saul all produced

25:10 a usable meeting reschedule message. For normal knowledge work, Terra is the balanced choice when you need stronger reasoning across several conditions and you still want good speed and cost efficiency. But do not assume Terra automatically beats Luna on every medium task. In our meeting notes test, both models handled the contradiction correctly. For deeper reviews across

25:34 several connected documents, Saul can add value through completeness and edge case analysis. But again, the bigger model does not automatically invalidate the smaller one. In our contract test, Terra got the core financial answer right. Saul went further and produced the more exhaustive audit. Use work when the goal is no longer answer my question. Use it when the goal is take

25:59 this project and produce the deliverable. But define the finish line, outcome, sources, deliverables, constraints, verification, and give the agent a stopping condition when missing information can change the result. For normal prompts, remember the six checkpoints. role, task, [music] context, constraints, format, model

26:21 choice, and keep three accuracy habits nearby. Ask important questions before recommending. Separate facts from assumptions. Run a skeptical second pass before finalizing important work. Here is the practical exercise. Open five recent Chad GBT conversations. Look at the task you gave the model. Was it actually a task or was it just a topic? Did the model know what finished look

26:46 like? Did it have the context that could change the decision? Did you define the constraints that could make the answer unusable? Then take one disappointing prompt and rebuild it. Do not make it longer just to make it look advanced. Add only the context that changes the result. Add the boundaries that prevent predictable mistakes. Ask for a format you can actually use. Then choose one

27:10 project for work, not your entire business. one project with a clear finish line, a research brief, a comparison document, a spreadsheet analysis, a travel feasibility report. Define the deliverables before the agent starts. Tell it what needs sources. Tell it what must be verified and tell it when to stop rather than guess. Finally, save three follow-up prompt somewhere

27:35 you can reuse them. Ask what important information is missing. Remove unsupported claims and run a skeptical self-critique before you finalize important work. The biggest skill with GPT 5.6 is not memorizing which model is supposedly the smartest. The real skill is matching the level of reasoning to the cost of the mistake. A small model

27:58 with a clear task can beat the flagship model with a vague prompt. A strong model can still make assumptions when the context is missing. And an agent can spend minutes doing the wrong work if you never define what done means. So start with one real task. Choose the model based on the difficulty of the work. Use the six-part framework when the answer matters. Use outcomebased

28:22 prompting when you delegate a project to work. And always make uncertainty visible instead of letting the model hide it inside a polished answer. Every prompt from this video appears on screen. Pause the video, copy one, and test it on something you actually need to do. Then compare the result with the way you were prompting before. That difference is where the real upgrade is.

28:45 The biggest upgrade in GPT 5.6 is not simply that there is a smarter model. It is that you now have different levels of reasoning for different levels of work. Luna can handle simple tasks without wasting resources. Terra can take on more structured everyday work. Soul can go deeper when completeness and edge cases matters and work can take a clearly defined project and turn it into

29:07 an actual deliverable. But none of those tools can fix a task that was never clearly defined. The real skill is knowing what you need before you ask. Choose the right model. Give it the context that changes the answer. Set the constraints that prevent predictable mistakes. Define exactly what the finished result should look like. And when the task matters, make the model show you what it knows, what it assumes,

29:31 and what it still cannot verify. That is how you stop using chat GPT as a chatbot and start using it as an actual working tool. GPT6 Astra might be the biggest leap in AI we've seen in a while. It beats the new Claude Fable 5.1 on key benchmarks and can actually do work on your