Summit: The AI-Powered Study Copilot Transforming How I Learn

Category: education

Reading Time: 14 min read

Tags: EdTech, AI Education, Study Tools, Machine Learning, LangChain, RAG Architecture

Origin Story: Tackling Academic Overwhelm

Summit was born out of a familiar struggle: the overwhelming flood of information students and professionals face when trying to learn complex material. The founder experienced firsthand the pain of juggling lecture slides, research papers, and textbooks under tight deadlines. Feeling "academic overwhelm" is more common than not – in fact, nearly 94% of college students report feeling overwhelmed by their studies. The traditional approach to studying (re-reading notes, highlighting textbooks) often leads to inefficient learning and high stress. Cognitive science shows that passive review is ineffective, yet many default to it because of sheer overload or not knowing better techniques.

Summit's origin can be traced to one specific late night: Drew was preparing for a big exam with hundreds of slides and hours of lecture recordings. The epiphany came: What if an AI could serve as a "study copilot," summarizing the deluge of material, generating practice questions, and coaching along the way? The idea was an AI assistant that would sit by your side as you climb the mountain of learning (hence "Summit"), making the ascent more manageable and even enjoyable. By leveraging advances in natural language processing and retrieval, Summit aims to help students study smarter, not harder – bringing techniques like active recall, spaced repetition, and goal-tracking into one intelligent platform.

In short, Summit's mission is to combat information overload with AI, giving learners a fighting chance to master material efficiently. It's like having a personal tutor, note-taker, and motivator in one – exactly the kind of ally one needs when facing towering piles of coursework or dense technical content.

Product Breakdown: Features and Components

Summit is composed of several integrated features that together form an AI-driven study copilot:

Goal Tracker: At the start of a learning journey, users set goals – e.g. "Learn chapters 1-5 before Friday" or "Understand and memorize all key formulas in lecture slides"*. The Goal Tracker in Summit helps break this into a daily/weekly plan. It might suggest: "Monday – cover Chapter 1 (10 topics), Tuesday – Chapter 2," etc., and adapt as you progress. This feature isn't just a static to-do list; it's intelligent. If the user falls behind or struggles with certain topics (as detected by quiz performance or time taken), the Goal Tracker adjusts the plan. It also uses spaced repetition principles: if you've flagged certain concepts for review, it schedules them at optimal intervals to maximize retention. The interface shows goal completion as a progress bar climbing towards the "summit," providing a visual motivator. Think of it as your study Sherpa, keeping you on track and adapting the route when needed.

Flashcard Generator from Slide Transcripts: One of Summit's superpowers is turning passive content (like lecture slides or videos) into active learning tools. Users can upload slide decks or even audio/video transcripts of lectures. Summit then uses AI to generate flashcards from this content. It employs an LLM (via LangChain) that scans through the text and pulls out key facts, definitions, or questions that would be good for testing knowledge. For example, if a slide says "Photosynthesis converts CO2 and water into glucose and oxygen in chloroplasts," Summit might generate a flashcard: "Q: What are the inputs and outputs of photosynthesis, and where does it occur? A: Inputs: CO2 and water; Outputs: glucose and O2; Occurs in chloroplasts." By doing this, Summit applies active recall* – prompting the learner to retrieve information rather than just reread it. Studies have shown retrieval practice is highly effective at improving long-term retention. Summit essentially automates the creation of those retrieval practice tools (flashcards), saving students countless hours. The flashcards can be reviewed in the app, with an option to toggle "show answer" and self-rate how well you knew it (which feeds back into spaced repetition scheduling).

Tutor Chat (AI Q&A Assistant): This is a conversational AI feature where users can ask questions and get explanations in plain English (or any language of choice). It's like having a 24/7 tutor available. The Tutor Chat is powered by large language models and anchored in the user's own study materials. So a user might ask, "I don't understand slide 10, what's the significance of the Krebs cycle step 3?" The AI, using the context of the slide's content (retrieved via the RAG system, more on that soon), will explain the concept, perhaps rephrasing what the lecturer said and adding clarifications. It can also answer broader questions like "Give me an example of how to apply this formula" or even quiz the student ("Would you like me to ask you a question on this topic?"). The key is the Tutor Chat is context-aware* – it knows what course or material you're studying so it can tailor its answers. If it references outside facts, it cites sources (like a specific textbook or paper if available). This builds trust and also guides the student where to look for more detail. Essentially, Tutor Chat alleviates the frustration of being stuck: no more endless Googling or waiting to email a TA; answers are on-demand. It's also polite and encouraging, in line with Summit's ethos to keep students motivated.

* Content Ingestion Pipeline: While not a user-facing feature, worth mentioning: Summit has an ingestion system where users can upload various content – PDFs, slide decks, even record audio of a lecture. Summit then transcribes audio (using a speech-to-text model if needed), and indexes all content. This means you can centralize your course material in Summit. Once ingested, it's available for the other features (flashcard gen, tutor Q&A). The ingestion supports multiple formats and uses OCR for images containing text if needed. It's the first step in the Summit loop: get all the knowledge in one place.

* Progress & Performance Dashboard: Over time, Summit tracks your study sessions: how many flashcards reviewed, quiz scores, goals met, etc. The dashboard gives insights like "You have mastered 70% of the material" or "Concepts to review: Thermodynamics (last score 50%)." It's somewhat akin to the metrics language apps like Duolingo provide, but tailored to academic content. This closes the feedback loop: you can actually see your improvement and identify weak spots.

Architecture: Next.js + LangChain + RAG Loop

Summit's architecture combines a modern web app frontend with powerful AI/ML backend components, orchestrated through a Retrieval-Augmented Generation (RAG) pattern to ensure accurate and context-relevant AI help.

* Frontend: Summit uses Next.js 15 for the web interface, utilizing React for dynamic components and server-side rendering for initial loads. The UI library is shadcn/ui, which provides a cohesive set of accessible, sleek components (e.g., modals for flashcards, accordions for content sections, progress bars, etc.). The frontend is written in TypeScript and manages state using hooks and context (for things like current study session, user progress, etc.). It communicates with the backend via REST and WebSocket for real-time updates (like showing transcription progress or streaming AI answers).

* Backend & LangChain: The brains of Summit lie in a Python backend powered by FastAPI (for serving API requests) and LangChain for orchestrating language model calls. LangChain provides a framework to chain together prompts, manage memory, and interface with vector databases for context retrieval. For instance, the Tutor Chat uses a LangChain RetrievalQA chain: when the user asks a question, the system:

1. Embeds the question using an embedding model.
2. Queries a Pinecone vector store (which holds embeddings of the user's ingested content) to retrieve relevant context (e.g., the slide text or notes related to the question).
3. Feeds that context + the question into an LLM (like GPT-4 or an open-source model) to generate an answer.
4. Returns the answer to the user, possibly with citations if the context had identifiable sources.

This RAG loop (Retrieval-Augmented Generation) ensures the AI's answers are grounded in the actual study material, reducing hallucinations and making the help very specific to the user's needs. The architecture can be visualized as:

User Query → [Next.js] → API → [LangChain] → Pinecone (retrieve docs) → LLM (OpenAI or local) → Answer → back to user (streamed)

Additionally, for flashcard generation, a similar process occurs: the system takes a chunk of content (e.g., a slide's text) and uses a prompt like "Generate a question-answer pair that tests understanding of this content" to an LLM. By templating this prompt, it can produce flashcards systematically for each chunk of content. The results are stored so that the user can review/edit them as needed.

* Vector Database (Pinecone): All ingested content is chunked (split into, say, paragraphs or slide bullets) and converted to embeddings using a model like OpenAI's text-embedding-ada or similar. These embeddings (vector representations) are stored in Pinecone, a hosted vector database optimized for similarity search. When the Tutor Chat needs context, it does a semantic search in Pinecone using the question's embedding to find the top relevant chunks. Pinecone guarantees this search is fast (< 200ms typically) even if the user has thousands of chunks, and it handles the heavy lifting of approximate nearest neighbor search. It's a key part of making Summit scale – whether the user has 10 pages of notes or 1000, retrieval will be quick.

* LLM Models: Summit is model-agnostic but currently uses OpenAI's GPT-4 (or GPT-3.5) via API for generating answers and flashcards, due to their strong capabilities. However, it also supports plugging in open-source models for self-hosted environments (like Llama 2 or other fine-tuned educational models) – important for institutions that might want to deploy Summit with proprietary data entirely on-premises for privacy. LangChain makes swapping models easier. Summit keeps track of token usage to display to users (especially if they're using their own API key or if there's a cost quota).

State & Memory: For the Tutor Chat, Summit uses LangChain's mechanisms to maintain conversational memory in a limited way. It might keep the last few questions and answers (or a summary of them) as context, so you can ask follow-ups like "What about the next step of the process?*" and the AI knows it refers to the previous topic. This memory is usually ephemeral (not stored long-term, just in the session).

* Database: A relational database (PostgreSQL) stores user data – profiles, goals, list of uploaded materials, generated flashcards, progress logs (which flashcards were marked easy/hard). The separation is that heavy text search is in Pinecone, whereas structured progress data is in Postgres.

* APIs and Services: Summit also interfaces with external APIs for certain features. For transcription of audio lectures, it might use something like Whisper API. For OCR of PDFs/slides, an OCR service (maybe Tesseract or an API like Google Vision if needed). These are integrated into the ingestion pipeline.

RAG Loop Explanation (for technical clarity): The RAG loop is central to Summit's architecture and worth a deeper note. Traditional LLM usage might try to stuff all notes into the prompt ("stuffing knowledge into the context window"), but that's not scalable for large courses and can be very costly. Instead, RAG smartly retrieves only relevant chunks*. Summit's loop has four steps: Ingestion, Retrieval, Augmentation, Generation:

1. Ingestion: All course data is chunked and embedded into Pinecone when uploaded.
2. Retrieval: On a question, relevant chunks are retrieved by vector similarity.
3. Augmentation: Those chunks are added to the LLM prompt (e.g. a system prompt that says "Use this context to answer user's question" with the retrieved text).
4. Generation: The LLM generates a answer grounded in that context.

This way, the model effectively has an extended, external knowledge base without needing an enormous context window. It's analogous to how a student might flip open the textbook to the right page before answering a homework question – Summit flips those pages automatically for the AI. This architecture is powerful: it can easily incorporate new data (just add more vectors) and keeps the model outputs accurate and citeable.

* Infrastructure: Summit likely runs in the cloud (for example, on AWS or similar). Next.js front and FastAPI/LangChain back can be containerized. Because of possibly high computational needs for AI, using serverless GPUs or having a scalable backend is considered (if demand is high, the heavy LLM tasks could be farmed out to an autoscaling service or a GPU worker pool). Pinecone is managed so it scales horizontally as data grows. The design keeps user-specific data siloed by user IDs, as privacy is paramount (your notes and content are only accessible to you and your AI sessions).

Feature UX: From Ingestion to Flashcards to Goal-Setting

Let's walk through a typical usage flow, showing how the features come together for the user:

1. Content Ingestion: Maria, a medical student, signs up on Summit. She creates a "Biochemistry 101" workspace and uploads her lecture slides (as PDFs) and a few lecture audio recordings. Summit immediately starts processing: it extracts text from the PDFs, transcribes the audio (with a progress indicator "Transcribing Lecture 3… 80%"). Within a few minutes, all the content is indexed in Summit. Maria sees a list of chapters and topics identified automatically (Summit might auto-group content by lecture or chapter based on filenames or headings).

2. Flashcard Generation: Maria clicks "Generate Flashcards" for Chapter 1 slides. Summit's AI churns for a moment and then pops up, say, 15 flashcards in a list. She reviews them: each card has a question and answer. Some are straightforward definitions (Q: "What is ATP?" A: "Adenosine triphosphate, the cell's energy currency"), others are conceptual (Q: "Why is the folding of protein X important?" A: "Because… [explanation]"). If any flashcard looks off, Maria can edit or discard it. She's effectively getting a distilled quiz prep without writing questions herself.

3. Study Session (Flashcard Mode): She begins a study session. Summit goes into a flashcard quiz mode (maybe full-screen distraction-free). It shows a question, Maria tries to recall the answer, then she clicks to reveal it and rates her knowledge ("Got it" or "Struggled"). Summit records this. This active recall session reinforces her memory – it's like using Anki or Quizlet, but the cards were generated on the fly from her material.

4. Tutor Chat for Clarification: While reviewing, Maria hits a tricky concept – say, the slide text on "Michaelis-Menten kinetics" is confusing. She opens the Tutor Chat and asks, "Can you explain in simple terms what Vmax and Km are?" The AI, referencing the lecture notes that mentioned those terms, responds with a clear explanation: "Vmax is the maximum rate of the reaction when the enzyme is saturated with substrate; Km is the substrate concentration at which the reaction rate is half of Vmax, a measure of enzyme affinity…" It also perhaps draws an analogy to make it simpler. Maria follows up, "Why does a low Km mean high affinity?" The AI remembers the context and elaborates appropriately, even referencing an earlier example it gave. This conversational loop helps Maria grasp a concept that the lecture slide alone didn't fully clarify.

5. Goal Setting and Tracking: At the start of the week, Maria set a goal "Finish and understand Chapters 1-3 by Sunday." Summit's Goal Tracker shows that she's now 33% through (having done Chapter 1). It might prompt her: "Do you want to schedule Chapter 2 for tomorrow?" She agrees. The next day, she gets a reminder notification. Summit keeps her accountable. If by Friday she hasn't touched Chapter 3, Summit will adjust: maybe suggest splitting Chapter 3 into Saturday and Sunday to still meet the deadline.

6. Performance Feedback: After a couple of weeks, Maria checks the dashboard. It says: "You've studied 10 hours with Summit. Flashcards mastered: 120. Your weakest topic: Enzyme Kinetics (60% average quiz score). Consider reviewing 5 flashcards from this topic today." This insight guides her to allocate time efficiently – focusing on weaknesses. She appreciates how Summit is not just a static tool, but an active coach giving her data-driven advice.

7. Summit Anywhere: She also installed the Summit mobile app (or used the responsive web app on her phone). On her commute, she can quickly run through a few flashcards or even listen to an AI-generated summary of a lecture (Summit can generate a summary and use text-to-speech). The cross-device sync means her progress is always up to date.

From ingestion to learning to feedback, Summit provides a cohesive loop:
Ingest content → Generate learning artifacts (flashcards) → Practice & Query (flashcards + tutor) → Track progress → Go back to more content or review as needed.

Technical Implementation: Prompt Engineering, Token Costs, and LangChain Example

Summit's AI features rely heavily on prompt engineering and efficient use of tokens (since large language model usage costs money and time). Here are some implementation insights:

* Prompt Engineering for Flashcards: To generate flashcards, the system uses a carefully crafted prompt template to the LLM. For example:

"You are an educational assistant that creates flashcards for study. Read the following lecture excerpt and generate a question that would test a student's understanding, and provide the correct answer. The question should be concise and the answer should be a factual, clear explanation. Use the student's material directly for accuracy, and don't introduce new info beyond the content.\n\[Lecture Excerpt]\n…(text)…\nQ: …? A: …."

By doing this, the LLM is guided to produce Q&A pairs that are grounded in the excerpt. It's instructed not to hallucinate beyond the content. The template might include examples in a few-shot manner (like one example flashcard to set the style). Getting this prompt right took some iteration – early versions might have produced too simple or too hard questions, or answers that were too verbose. Through testing, the team refined the prompt until the flashcards were high-quality out of the box.

Tutor Chat Prompt: For the tutor, a system message is set at the start of the chat, something like: "You are Summit, an AI study tutor. You have access to the user's study materials (slides, notes) as context. Your job is to answer questions accurately using that content. Be thorough but clear. If relevant, cite the source or slide. If you don't know or the answer isn't in the materials, encourage the student to check their resources or offer to help reason it out." Then each user query triggers the retrieval of context which is prepended with something like "Context:\n[relevant text]\nUser's question: {question}\nAnswer as tutor:". This ensures the LLM knows to use the provided context. The model's style is also defined to be encouraging and not just dumping info. For example, it might end an explanation with "Does that make sense? Feel free to ask if you need more clarification."* to imitate a good human tutor.

* Token Cost Optimization: Using GPT-4 is powerful but comes with cost. Summit takes steps to minimize tokens:

* During retrieval, only the most relevant chunks are included (maybe top 3 chunks). This limits prompt size. If each chunk is ~200 tokens and the question is 20, context might be ~600 tokens plus some overhead, which GPT-4 can handle and keeps cost lower.
* Summit might use GPT-3.5 for less critical tasks (like flashcard generation or summarization) as it's cheaper, and reserve GPT-4 for nuanced Q&A where quality matters more.
* Also, any static instructions are kept concise. The team measured that a typical tutor chat answer with context might use ~800 prompt tokens and ~300 response tokens. At current rates (say $0.003 per 1K tokens for input, $0.004 per 1K for output on a GPT-3.5, hypothetically), that's about ~$0.0036 per answer – very reasonable. Even GPT-4, if say $0.03/1K, would be around $0.03 per answer of 1000 tokens. Over a study session of dozens of questions, it can add up, so Summit tracks usage. Possibly, they give users a certain free quota and then might require a subscription if usage is heavy (monetization consideration).
* There's also caching: if two users have the same material and ask a similar question, Summit could recognize that and reuse a previous answer after verifying it (though this is tricky with user-specific data).

* LangChain Example Code: For a concrete glimpse, here's a pseudo-code snippet of how Summit might use LangChain for the Q&A:

``python
from langchain.chains import RetrievalQA
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.llms import OpenAI

# Initialize embedding model and vectorstore (Pinecone index)
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")
vectorstore = Pinecone(indexname="summit-index", embeddingfunction=embeddings.embedquery)

# Create a QA chain that will use the OpenAI LLM with my vector store retriever
qachain = RetrievalQA.fromchain_type(
llm=OpenAI(modelname="gpt-4", temperature=0, maxtokens=300),
chain_type="stuff", # it will "stuff" retrieved docs into prompt
retriever=vectorstore.asretriever(searchkwargs={"topk": 3}),
returnsourcedocuments=True # to possibly cite sources
)

# Example usage:
query = "Explain the significance of the Krebs cycle's third step."
result = qa_chain({"query": query})
answer = result["result"]
sources = result["source_documents"]
print("AI Answer:", answer)
for doc in sources:
print("Source snippet:", doc.metadata.get("source"), "-", doc.page_content[:100])
`

In this snippet:

* I configure an OpenAIEmbeddings to handle query embedding (assuming the content is already indexed in Pinecone with same embedding model).
* I wrap OpenAI LLM (GPT-4) in a RetrievalQA chain.
* I specify chain_type="stuff" meaning it will just stuff the retrieved docs into the prompt (LangChain also has chain types like map-reduce for summarization, etc., but here stuffing is fine for Q&A).
* top_k: 3 means retrieve 3 chunks.
* I enable returnsourcedocuments for transparency, though Summit might not always display them directly to the user, it could use them to cite or for debugging.

Then calling qa_chain with a query does the whole RAG workflow: embed query, search Pinecone, construct prompt, get answer. The answer variable holds the AI's response, which Summit then displays in the chat UI. The sources` list holds the retrieved chunks – Summit could use these to show citations or at least internally ensure the answer is grounded.

This kind of code is at the heart of Summit's tutor feature. It's remarkably concise thanks to LangChain, even though under the hood it orchestrates complex steps. The Summit team likely built on this, adding custom prompt templates and guardrails (like possibly a function to rephrase the question if initial answer is "I don't know" – maybe by broadening the search).

* Handling Limits and Errors: The implementation also accounts for things like what if the answer is too long (Summit might break it into parts, or for flashcards ensure answers aren't essays). If the LLM ever fails (rate limit or error), Summit catches that and shows a friendly message, perhaps falling back to a simpler explanation or asking the user to retry.

* Token Counting: Summit keeps an eye on tokens. Libraries like tiktoken can count tokens of prompts beforehand to ensure they don't exceed model limits (e.g., GPT-4's context). Also, an approximate counter is used to estimate cost in real-time – some advanced users like to see "This answer used ~~1,100 tokens (~~$0.03)" as a transparency feature. Summit's philosophy is user-centric, so being open about something like that aligns with trust-building.

Pilot Results & Anecdotal Performance

Summit has been piloted with a small cohort of students at different levels, and the feedback has been promising:

In an initial pilot with 20 students (mix of undergrads and grad students in various fields), 85% reported studying more efficiently and with less stress. They particularly loved the flashcard generator; one student said, "Summit saved me at least 5 hours making flashcards. And they were good questions I might not have thought of myself."*

* Pilot students saw tangible improvements: in one case, a student's exam score improved from the 70s to 85 after using Summit for two weeks. While that's anecdotal and many factors influence grades, the student credited Summit for better retention of details.

* Usage data from the pilot showed that the Tutor Chat was used heavily – on average, each student asked 15 questions per week. Interestingly, many questions were not "factual" but "explain this differently" or "give me an example," indicating Summit helped with deeper understanding, not just rote answers.

Another anecdote: a medical resident used Summit to prepare for a board exam. He had thousands of pages of notes. Summit's goal tracker and spaced flashcards helped him organize the 3-month study plan. He noted that having the AI to discuss tough concepts at 2 AM was a game-changer*, joking that "it's like I had a study buddy who never sleeps."

* Performance-wise, Summit's system held up in the pilot with minor hiccups. There were a few instances where the AI gave an incorrect answer (usually due to not finding the right context). But thanks to the RAG approach, most answers were accurate. When mistakes were found, users could flag them – this feedback loop is used to improve prompts or add content to the knowledge base.

* On the engineering side, average response time for a tutor question was ~5 seconds with GPT-4 (mostly due to the model's latency). Some impatient users felt this was slow compared to a Google search, but when they realized the quality of answer, they didn't mind the wait. Summit might implement a "fast mode" with a smaller model for quick factual Qs to address this.

* Token usage in pilot: The average student in a week used around 50k tokens of AI responses. At current costs that might be a couple of dollars – completely viable for a service to sustain via a subscription model.

These results, while early, indicate that Summit not only resonates with users but genuinely helps them study better. The qualitative feedback ("I feel less anxious about my exam now" was a common theme) speaks to Summit's potential in improving learners' experiences, not just their scores.

Roadmap: The Summit Ahead

Summit's journey is just beginning. The roadmap includes ambitious features to make the study copilot even more powerful and accessible:

1. Spaced Repetition Integration: While Summit schedules some reviews, the plan is to fully integrate a spaced repetition algorithm (like SM2 used by Anki) for flashcards. This means Summit will automatically schedule flashcard reviews at optimal intervals (1 day later, 3 days, 1 week, etc., depending on whether you got it right or struggled). It will essentially maintain an ever-evolving deck of flashcards that adapt to your learning curve, maximizing long-term retention.

2. Mobile App & Offline Mode: A dedicated mobile app is high priority, since studying isn't confined to the desktop. The app would support offline access for notes and flashcards (so you can review on the subway without internet). AI features might require connection unless a small on-device model is used for some tasks. The mobile UI will focus on quick reviews and asking the tutor simple questions on the go.

3. AR/VR and Multimodal Learning: Looking further ahead, Summit explores multimodal features. For instance, using the phone's camera to take a photo of a textbook page or a diagram and then asking the AI tutor to explain it. Or even AR glasses support: imagine looking at a complex diagram through glasses and the AI labels parts for you. These are exploratory ideas on how Summit can leverage vision models to enhance studying of visual materials.

4. Voice and Listening Mode: Integrating a voice-based interface where you can essentially talk to Summit. "Hey Summit, quiz me on Chapter 4" and it will ask questions out loud, listen to your answer (using speech recognition), and then evaluate or continue the conversation. This would be great for language learning or for people who want to study while, say, driving or walking (turning dead time into study time, hands-free).

5. LMS Integrations: Summit aims to integrate with Learning Management Systems (like Canvas, Blackboard, Moodle). This would allow it to automatically pull in course content (readings, slides) without the student having to upload, and possibly even push back analytics (like if permitted, telling the LMS that a student has completed certain study milestones). For institutions, this integration could make Summit a complement to official course material, maybe even as an offering to help students who need extra support.

6. Community Sharing (Opt-in): A feature under consideration is letting users share anonymized flashcard sets or notes with others in the same class or studying the same subject. A community aspect could help students not feel alone and also crowdsource improvements (like if 100 students generate flashcards on the same topic, Summit could aggregate the best ones). Of course, privacy is key, so it would be opt-in and perhaps moderated.

7. Continuous Learning & Personalization: The AI tutor will get more personalized with prolonged use. If it learns that you prefer certain explanation styles (maybe you like analogies, or you prefer mathematical rigor), it could adapt answers to fit your style. Also, detecting if a student is frustrated (maybe by tone of questions or quick repeats) to then adjust approach (perhaps injecting encouragement or breaking down the explanation more).

8. Expanded Knowledge Base: Right now Summit relies on user-provided content. In the future, it might integrate with external knowledge bases or textbooks (with permission or public domain sources) so that even if something isn't in your notes, the AI can fetch it (with citations). For instance, if you ask a question that your notes don't cover but a standard textbook does, Summit could pull that in. This would blur the line between your personal notes and general knowledge, essentially filling gaps for you.

Call to Action (CTA): Summit is on a mission to transform how we learn. The team is actively improving the platform and expanding access. If this sounds exciting, you can check out Summit's GitHub repository for a closer look at the code, or visit the live demo on my website to try the copilot on your own study material. I welcome contributors, feedback, and collaboration – together, let's reach the summit of learning efficiency!

Summit's journey embodies an analytical yet humanistic approach: using cutting-edge AI to solve real-world student problems in a plain-English, user-friendly way. It recognizes that learning is a climb, but with the right partner, that climb can lead to breathtaking views of understanding. Give Summit a try and let it shoulder some of your study load – your future self (and GPA) might thank you.