How mentorAI Integrates with Groq
mentorAI plugs into Groq’s OpenAI-compatible LPU API so universities can route any mentor to ultra-fast models like Llama 4 Maverick or Gemma 2 9B that stream ~185 tokens per second with deterministic sub-100 ms latency. Admins simply swap the base URL or point at an on-prem GroqRack, while mentorAI enforces LlamaGuard safety and quota tracking across cloud or self-hosted endpoints such as Bedrock, Vertex, and Azure—no code rewrites.
mentorAI now taps Groq’s Language Processing Units (LPUs) for lightning‑fast inference, turning AI mentors, coding labs, and assessments into real‑time experiences. Here’s the streamlined overview.
Groq Models in mentorAI
- Llama 4 Scout – ultra‑fast, compact model for real‑time chat, quizzes, and autocomplete.
- Llama 4 Maverick – flagship Groq‑tuned 70 B model that pairs long‑context reasoning with sub‑100 ms latency.
- Llama 3.3 70B Speculative Decoding – experimental variant that uses Groq’s deterministic pipeline for even higher throughput on large context prompts.
- Llama‑3.3‑70B‑Versatile (128 K) – deep reasoning, long‑context tutoring, essay feedback.
- Llama‑3.1‑8B‑Instant (128 K) – sub‑100 ms replies for high‑volume chat and quick Q&A.
- Llama‑Guard‑3‑8B – safety‑tuned variant for content filtering and compliant grading.
- Gemma 2‑9B‑IT – Google’s 9 B technical model for code and IT labs.
- Mistral Saba 24B (32 K) – multilingual tutor for Arabic, Urdu, Hebrew, Indic languages.
- Whisper V3 – speech‑to‑text for lecture transcripts and voice chat.
Deployment & Routing
1. Plug‑and‑play API – change the OpenAI base URL to [https://api.groq.com/openai/v1](https://api.groq.com/openai/v1) and add a Groq key. 2. Model mapping – admins assign each course/mentor to a Groq model; mentorAI’s middleware auto‑routes and load‑balances. 3. On‑prem option – schools with strict data rules can run a GroqRack™ in their data center; mentorAI points at the private endpoint. 4. Batch jobs – bulk content generation or grading runs through Groq’s JSONL Batch API for lower cost and higher throughput.Prompt Orchestration & Controls
- Persona prompts define tone (coach, grader, lab assistant).
- Context injection feeds syllabi or full lectures (128 K context) for accurate answers.
- Function calls / JSON mode let mentors trigger tools (calculators, code runners).
- Safety layer chains LlamaGuard plus mentorAI filters before students see output.
Monitoring, Cost, Privacy
mentorAI logs tokens, latency, and errors for each Groq call, enabling:- Real‑time SLA alerts if latency drifts above 100 ms.
- Per‑model quotas and spend dashboards.
- Full transcript audit trails (encrypted at rest); data never leaves the institution when using GroqRack.
Why Groq Matters for Higher Ed
- Instant feedback – >300 tokens/s on 70 B models keeps chats, quizzes, and code hints truly interactive.
- Scalable classrooms – deterministic LPUs keep latency low even with hundreds of concurrent students.
- Cost efficiency – LPUs deliver 10× higher tokens/W than GPUs, stretching limited edtech budgets.
- Future‑proof – as Groq adds new models or larger context windows, mentorAI adopts them via a simple config switch.
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How mentorAI Integrates with Blackboard
mentorAI integrates with Blackboard Learn using LTI 1.3 Advantage, so every click on a mentorAI link triggers an OIDC launch that passes a signed JWT containing the user’s ID, role, and course context—providing seamless single-sign-on with no extra passwords or roster uploads. Leveraging the Names & Roles Provisioning Service, Deep Linking, and the Assignment & Grade Services, the tool auto-syncs class lists, lets instructors drop AI activities straight into modules, and pushes rubric-aligned scores back to Grade Center in real time.
How mentorAI Integrates with Brightspace
mentorAI plugs into Brightspace via LTI 1.3 Advantage, letting the LMS issue an OIDC-signed JWT at launch so every student or instructor is auto-authenticated with their exact course, role, and context—no extra passwords or roster uploads. Thanks to the Names & Roles Provisioning Service, Deep Linking, and the Assignments & Grades Service, rosters stay in sync, AI activities drop straight into content modules, and rubric-aligned scores flow back to the Brightspace gradebook in real time.
How mentorAI Integrates with Amazon Web Services
mentorAI runs natively on AWS: it taps Amazon Bedrock’s fully managed API to access Titan, Claude, Llama and other foundation models without universities having to manage GPUs, while its containerized micro-services auto-scale on ECS Fargate to keep response times steady during peak weeks and store tenant-segregated transcripts in RDS Postgres/Aurora silos or schemas protected by VPC/IAM boundaries. This architecture lets campuses spin up pilots or university-wide deployments, maintain FERPA/GDPR data sovereignty, and adopt any new Bedrock model with a simple config switch.
How mentorAI Integrates with Meta
mentorAI treats open-weight Llama 3 as a plug-in backend, so schools can self-host the 8B/70B checkpoints or point to 405B cloud endpoints on Bedrock, Azure, or Vertex with one URL swap. LlamaGuard plus mentorAI filters keep chats compliant, while open weights let faculty fine-tune models to campus style and run them locally to avoid usage fees.