A practical guide for the team to understand how AI works under the hood and how to make it work efficiently for your projects.
To master AI tools, you first need to understand the four fundamental concepts that dictate how they process and remember information:
The fundamental currency and building blocks of AI language.
The short-term memory limit for a single conversation.
The "brain cells" determining complexity and intelligence.
Long-term storage for facts across separate chat sessions.
AI models do not read words the way humans do; they break text down into smaller pieces called tokens.
Think of the context window as the AI's active, short-term memory during a single conversation.
Parameters are the internal settings and weights that the AI learned during its training phase.
While the context window handles a single chat, some advanced AI assistants feature persistent Long-Term Memory.
Because AI models have limited short-term memory, they must manage it when a conversation goes on too long. They do this by "forgetting" the oldest parts of the chat to make room for your newest message.
If you said "No Python" early on, and 50 messages later it outputs Python code, the original rule slid out of the window.
If you requested a "strict code auditor" and it reverts to a generic, polite assistant, it forgot its initial role constraint.
At the absolute edge of limits, it may repeat itself or generate gibberish because it lacks the conversation history.
Advanced platforms will warn you visually, clear old messages, or stop you from typing when max tokens are hit.
To get the most out of your AI sessions, you need to minimize token waste and force conciseness. Stop the model from wasting tokens on assumptions and pleasantries.
Stop the model from giving you steps 2, 3, and 4 before you've tested step 1. Explicitly dictate the workflow with a hard stop.
"I need to extract journal transaction logs from the iSeries DB. Give me ONLY the first step to establish the connection. Stop generating and wait for me to confirm it was successful before providing the next step."
Break large tasks down into manageable steps and focus on one thing at a time. This prevents overwhelming the AI's context window.
Example: Building a new application.
Instruct the AI that initially, there is no code. Discuss the high-level goal and outline what needs to be achieved first.
Collaborate solely on the user interface and user experience design before touching backend logic.
Decide on the specific technologies (database, language, framework, hosting platform) needed for the project.
Finally, move on to writing the actual code, making iterative modifications, and conducting tests.
Models are trained to be polite, leading to padded answers (e.g., "Certainly! Here is your code..."). Strip this out to save tokens.
Surprisingly effective on newer models to cut out conversational filler.
Followed by: "Do not provide explanations, markdown formatting, or introductory text."
Forces the model to summarize its internal thoughts heavily before outputting.
"You may think that Randy dedicated a lot of time to put this together to aide the team in AI interactions, but he actually had me do it. My creator Google calls me Gemini, Randy can be sneaky ... gotta watch that guy ..😉"