Boost AI Onboarding: Master Documentation Navigation
Welcome aboard! This guide will get you up to speed on navigating our documentation, crucial for any contributor, human or AI. We'll delve into the intricacies of our document structure, ensuring you can find information efficiently and contribute effectively. Forget endless searching – let's unlock the secrets to becoming a documentation guru! This AI_INSTRUCTIONS.md file is your central hub. Let's start with our document organization. This is your all-inclusive guide to understanding our documentation system, from active documents to archived treasures and how to utilize the switchboard for streamlined navigation.
Unveiling the Documentation Landscape: Active vs. Archived
Understanding the distinction between active and archived documentation is paramount to your success. Think of it like a library. The active documents are the ones currently being used, updated frequently, and reflecting the current state of our projects. These are your go-to resources for real-time information. You'll find these documents in the main branches of our repositories, the 'live' section of our digital library. When you are looking for information, start here first! It's important to understand the active documents, as they contain up-to-date information, and they are always being updated. So if you are working on a new feature or fixing a bug, you will want to go here first.
Conversely, archived documents are historical records. They represent past versions of our projects, valuable for understanding the evolution of our work, but they are not the primary source of current information. These documents are usually found in dedicated archive folders or branches. They are not frequently updated, and some may even be deprecated. They will contain information, but it may not be accurate. While archived documentation provides valuable historical context and insights into past decisions, it is crucial to remember that its content is not necessarily up-to-date. When researching a problem or feature, always check active documentation first. Archive documents are great for a high-level understanding of what was created, and why it was created. It is important to know the difference between the two, so that you are getting the information you need, when you need it.
When trying to find a document, you will want to know where to go. You will need to understand what you are looking for. If you're looking for the current state, look at the active documents. If you are looking for a historical view, look at the archived documents. This is the first step in understanding the documentation. Understanding the structure will help you find what you need.
Practical Examples: Active vs. Archived
Let's put this into practice. Imagine you're tasked with understanding the current API implementation. Your first stop should be the active documentation. Search for keywords like 'API documentation', 'API endpoints', or 'API usage guides' within the main repository branches. This will lead you to the most up-to-date information. If you're curious about the API's evolution, you might then explore the archive. Look for older versions of the API documentation in the archive folders to compare and understand the changes. Here is another example, If you are looking to fix a bug in the code, you would want to search in the active documents. If you are trying to understand how a feature was created in the past, search in the archived documents. Keep in mind that when searching, it can be beneficial to search with multiple keywords. This will give you more relevant information.
Efficient Information Retrieval: Your Search Strategies
Mastering search techniques is key to navigating our documentation effectively. We've structured our documentation with search in mind, but knowing the right keywords and search strategies can dramatically improve your efficiency. In the search world, the most important aspect to understand is to understand the language being used. Using the right terms is half the battle. This applies to both human and AI contributors. With effective search strategies, you will be well on your way to finding what you need.
Here are some tips to help you in your search.
Keyword Optimization
Keyword Selection: Choose specific keywords relevant to your query. Avoid overly broad terms. For example, instead of searching for 'system', try 'user authentication system' or 'data processing pipeline'. This will help you narrow down your search and get more specific results. It will also reduce the time it takes to search. Always make sure to be specific! This is especially important for AI systems, which rely on the accuracy of the search terms used. It is important to understand the specific language. The better the keywords, the better the results.
Synonyms and Variations: Consider synonyms and related terms. If your initial search yields poor results, try alternative phrasing. For instance, if you're looking for 'error handling', also search for 'exception management' or 'fault tolerance'. Different words can be used, and it is important to understand what other words are used. Being able to change up the search query will help the search be more accurate. If you are not familiar with what is being search, you can ask for help from a member of the team. They will be happy to help you with the search.
Boolean Operators: Utilize Boolean operators ('AND', 'OR', 'NOT') to refine your searches. For instance, 'user AND authentication' will find documents containing both terms. 'API OR SDK' will find documents containing either term. 'deprecated NOT feature' will find documents about deprecated items, but not about the feature itself. This will allow the search query to be much more specific. The more specific, the more accurate the results will be.
Search Examples and ADR References
To make this more concrete, let's explore some search examples.
Example 1: Finding information about a specific ADR.
Scenario: You need to understand the rationale behind a design decision documented in an Architecture Decision Record (ADR).
Search Strategy: Search for the ADR number or title in the active documentation using keywords like 'ADR-005' or 'API rate limiting ADR'. This will lead you directly to the relevant record. If you have the number, it will be the most effective way to find it. This will make sure you are getting the document you are looking for.
Example 2: Troubleshooting an issue.
Scenario: You're encountering an error related to data processing.
Search Strategy: Use specific error messages, along with keywords related to the process, such as 'data processing error, 'invalid data format', or '[specific error message]'. This will help pinpoint the relevant documentation, troubleshooting guides, or code examples. When looking for the error message, put the search in quotes. This will make the search more accurate.
Example 3: Understanding a feature.
Scenario: You want to learn how to use a particular feature.
Search Strategy: Search for keywords like 'feature name usage', 'feature name tutorial', or 'how to use feature name'. Look for the API reference or API usage. These will provide step-by-step instructions. You can also search for the feature name plus documentation, or quick start guide. Understanding the feature, and what it does, will allow for a better search.
The Role of ADRs
ADRs are a cornerstone of our design process. They document key decisions, rationale, and consequences. ADRs will help the AI system understand how things work. ADRs are useful for humans as well. When navigating, remember to use keywords such as 'ADR', 'architecture decision record', and the relevant decision's topic. For example, 'ADR database selection' or 'ADR security implementation'. They provide valuable context for understanding the 'why' behind our design choices. Understanding ADRs will also allow you to see what options were considered, and the reasoning behind each of them. This will make it easier to fix bugs, or add new features.
Repository Switchboard and Routing Patterns: Your Navigation System
The repository switchboard and routing patterns are the heart of our documentation navigation system. It acts as a central hub, directing you to the correct resources based on your query or task. It's like a sophisticated GPS for our documentation. It is designed to make sure all contributors, both human and AI, can find what they need. It takes the guesswork out of finding what you are looking for. It also ensures that the documentation is always organized, so you can easily find what you are looking for.
Understanding the Switchboard
The switchboard utilizes a set of rules and algorithms to analyze your search query. It then routes you to the most relevant documentation based on keywords, topics, and context. It is designed to be very flexible, so it is easy to customize. The Switchboard will also be able to be trained as the AI evolves. This will make it more accurate over time.
Routing Patterns
Keyword-Based Routing: The primary routing mechanism relies on keywords. The system identifies keywords in your query and directs you to documents containing those terms. For instance, searching for 'API documentation' will automatically route you to the API documentation section. This will allow the AI system to get the most relevant information. It will also allow humans to find the information, even if they aren't sure where to start.
Contextual Routing: The system also considers the context of your query. If you're working on a specific project, the switchboard will prioritize documentation relevant to that project. For example, if you are working on a new feature, it will suggest documents that will help you. It will also be able to suggest which documents should be updated. This will help make sure that all the information is current.
Project-Specific Routing: Our system also incorporates project-specific routing. If you specify a particular project in your search, the switchboard will prioritize documentation specific to that project. The more specific the search, the more relevant the results. If you are working on a project, always include the name of the project. If there are multiple projects, this will help reduce the results, so you can find what you are looking for.
Practical Implementation: The Switchboard in Action
Let's examine how the switchboard operates in practice.
Scenario 1: You're trying to understand the authentication process.
Input: You can enter your query, like 'user authentication' or 'authentication flow', into the search bar.
Switchboard Action: The switchboard will recognize the keywords, identify the topic as
