---
title: "AI-Engineering.academy vs rag-time"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/adithya-s-k-ai-engineering-academy-vs-microsoft-rag-time"
tools: ["adithya-s-k-ai-engineering-academy", "microsoft-rag-time"]
---

# AI-Engineering.academy vs rag-time

*GraphCanon updated Aug 24, 2026*

## Verdict

Pick AI-Engineering.academy if aI-Engineering.academy is an educational content repository specialized in the practical application of AI concepts using Jupyter Notebooks. It's ideal for learning about fine-tuning and serving large language models; pick rag-time if rAG Time is tailored for those looking to systematically learn and apply Retrieval-Augmented Generation techniques in a structured 5-week program.

[AI-Engineering.academy](https://aiengineering.academy) reports 2.4k GitHub stars, 276 forks, and 7 open issues, last pushed Feb 27, 2026. [rag-time](https://github.com/microsoft/rag-time) has 898 stars, 320 forks, and 4 open issues, last pushed Jun 17, 2025. Figures are from public GitHub metadata via [AI-Engineering.academy's repository](https://github.com/adithya-s-k/AI-Engineering.academy) and [rag-time's repository](https://github.com/microsoft/rag-time).

| | [AI-Engineering.academy](/tools/adithya-s-k-ai-engineering-academy.md) | [rag-time](/tools/microsoft-rag-time.md) |
| --- | --- | --- |
| Tagline | Mastering Applied AI, One Concept at a Time | RAG Time: A 5-week Learning Journey to Mastering RAG |
| Stars | 2,377 | 898 |
| Forks | 276 | 320 |
| Open issues | 7 | 4 |
| Language | Jupyter Notebook | Jupyter Notebook |
| Adopt for | AI-Engineering.academy is an educational content repository specialized in the practical application of AI concepts using Jupyter Notebooks. It's ideal for learning about fine-tuning and serving large language models. | RAG Time is tailored for those looking to systematically learn and apply Retrieval-Augmented Generation techniques in a structured 5-week program. |
| Persona | - | - |
| Runtime | - | - |
| License | Available under MIT license, allowing broad usage with attributions | The MIT License provides freedom to use, copy, modify and distribute the software provided that copyright and license information are retained. |
| Categories | Inference & Serving, LLM Frameworks, Model Training | Data & Retrieval, LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [AI-Engineering.academy](/tools/adithya-s-k-ai-engineering-academy.md) | [rag-time](/tools/microsoft-rag-time.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Dormant (18%) |
| Days since push | 177d | 431d |
| Open issues (now) | 7 | 4 |
| Stars delta | +14 (30d) | +5 (30d) |
| Owner type | User | Organization |
| Full report | [trust report](/tools/adithya-s-k-ai-engineering-academy/trust.md) | [trust report](/tools/microsoft-rag-time/trust.md) |

## Decision facts: AI-Engineering.academy

- **Hosting:** self hosted - The content is accessible directly through Jupyter Notebooks and does not require the setup of a separate server or environment.
- **Pricing:** freemium - Currently freely available, but as more features are added, some advanced modules might be behind a paywall.
- **Adopt for:** AI-Engineering.academy is an educational content repository specialized in the practical application of AI concepts using Jupyter Notebooks. It's ideal for learning about fine-tuning and serving large language models.
- **License detail:** Available under MIT license, allowing broad usage with attributions

## Decision facts: rag-time

- **Requirements:** Min 8 GB RAM
- **Adopt for:** RAG Time is tailored for those looking to systematically learn and apply Retrieval-Augmented Generation techniques in a structured 5-week program.
- **License detail:** The MIT License provides freedom to use, copy, modify and distribute the software provided that copyright and license information are retained.

## Choose when

### Choose AI-Engineering.academy if…

- The content is accessible directly through Jupyter Notebooks and does not require the setup of a separate server or environment.
- Pricing: Currently freely available, but as more features are added, some advanced modules might be behind a paywall..
- Tags unique to AI-Engineering.academy: fine-tuning, inference, large language models, quantization.
- Also covers Inference & Serving.
- - When you need hands-on, guided tutorials to understand how to fine-tune large language models with a focus on practical applications.

### Choose rag-time if…

- Requirements: Min 8 GB RAM.
- Tags unique to rag-time: ai, generative-ai, hybrid-search, indexing.
- Also covers Data & Retrieval.
- When you need a detailed, week-by-week learning path specifically focused on the nuances of RAG techniques, from basics to advanced applications.

## When NOT to use AI-Engineering.academy

- - Avoid this resource if you are seeking theoretical deep-dive content without practical applications; the focus here is on hands-on learning.
- - If your goal is to explore a wide range of AI-related topics beyond language models and inference, as this repository specializes narrowly in these areas.
- - Not suitable for individuals needing real-time personalized guidance from experts but rather prefer pre-crafted educational materials.

## When NOT to use rag-time

- If you prefer ad-hoc or self-directed learning without a structured timeline. Other tools may offer more flexible formats, which can be preferable if adhering to strict schedules is not ideal.
- When your focus is solely on either indexing or generation models and not the integration of both for RAG. In this case, specialized resources for just indexing or model training might suffice.

## Common questions

### What is the difference between AI-Engineering.academy and rag-time?

AI-Engineering.academy: Mastering Applied AI, One Concept at a Time. rag-time: RAG Time: A 5-week Learning Journey to Mastering RAG. See the comparison table for live GitHub stats and shared categories.

### When should I choose AI-Engineering.academy over rag-time?

Choose AI-Engineering.academy over rag-time when The content is accessible directly through Jupyter Notebooks and does not require the setup of a separate server or environment; Pricing: Currently freely available, but as more features are added, some advanced modules might be behind a paywall.; Tags unique to AI-Engineering.academy: fine-tuning, inference, large language models, quantization; Also covers Inference & Serving; - When you need hands-on, guided tutorials to understand how to fine-tune large language models with a focus on practical applications.

### When should I choose rag-time over AI-Engineering.academy?

Choose rag-time over AI-Engineering.academy when Requirements: Min 8 GB RAM; Tags unique to rag-time: ai, generative-ai, hybrid-search, indexing; Also covers Data & Retrieval; When you need a detailed, week-by-week learning path specifically focused on the nuances of RAG techniques, from basics to advanced applications.

### When should I avoid AI-Engineering.academy?

- Avoid this resource if you are seeking theoretical deep-dive content without practical applications; the focus here is on hands-on learning. - If your goal is to explore a wide range of AI-related topics beyond language models and inference, as this repository specializes narrowly in these areas. - Not suitable for individuals needing real-time personalized guidance from experts but rather prefer pre-crafted educational materials.

### When should I avoid rag-time?

If you prefer ad-hoc or self-directed learning without a structured timeline. Other tools may offer more flexible formats, which can be preferable if adhering to strict schedules is not ideal. When your focus is solely on either indexing or generation models and not the integration of both for RAG. In this case, specialized resources for just indexing or model training might suffice.

### Is AI-Engineering.academy or rag-time more popular on GitHub?

AI-Engineering.academy has more GitHub stars (2,377 vs 898). Stars measure visibility, not whether either tool fits your constraints.

### Are AI-Engineering.academy and rag-time open source?

Yes - both are open-source projects on GitHub (AI-Engineering.academy: MIT, rag-time: MIT).

### Where can I find alternatives to AI-Engineering.academy or rag-time?

GraphCanon lists graph-backed alternatives at [AI-Engineering.academy alternatives](/tools/adithya-s-k-ai-engineering-academy/alternatives) and [rag-time alternatives](/tools/microsoft-rag-time/alternatives) ([AI-Engineering.academy markdown twin](/tools/adithya-s-k-ai-engineering-academy/alternatives.md), [rag-time markdown twin](/tools/microsoft-rag-time/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/adithya-s-k-ai-engineering-academy-vs-microsoft-rag-time.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, AI-Engineering.academy or rag-time?

AI-Engineering.academy: Slowing. rag-time: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for AI-Engineering.academy and rag-time?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AI-Engineering.academy trust report](/tools/adithya-s-k-ai-engineering-academy/trust); [rag-time trust report](/tools/microsoft-rag-time/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=adithya-s-k-ai-engineering-academy`](/api/graphcanon/graph?tool=adithya-s-k-ai-engineering-academy)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_