yo, honestly been there... I've had great luck with these for ur situation: - SciSpace Academic Literature Review Tool: The Copilot is literally a lifesaver for methodology. It highlights the source text directly so no hallucinations. - Scholarcy Online Flashcard Generator: This one creates structured summaries and is great at pulling out the 'Limitations' section specifically. Both are wayyy more technical than GPT. hope it helps! 👍

yo, honestly i feel u on the thesis struggle... when i was grinding through my lit review last year, i almost lost it trying to keep methodology sections straight. basically wasted weeks before i found some technical shortcuts that dont cost a fortune. since the others mentioned the big hitters, i'd suggest checking these out: - Consensus AI Search Engine: the free tier is actually decent for evidence-based snapshots. its grounded in a database of 200M+ papers so it stays on track better than vanilla LLMs.
- Semantic Scholar: it's a free project from AI2. the 'TLDR' summaries are generated using specific NLP models trained on scientific text, so they're pretty reliable for quick filtering. - Perplexity AI: if u can afford a small sub, the Perplexity Pro lets u toggle between models. i suggest using the Claude 3.5 Sonnet setting—its reasoning on complex methodology is highkey more precise than gpt-4, right? just be careful and make sure to verify any 'hallucinated' data points. AI still struggles with specific stats sometimes, you know? gl!!

I struggled too... but Elicit AI Research Assistant lowkey works best: - custom data extraction
- semantic search Tbh, it's way more reliable than ChatGPT. lol

Regarding what #3 said about the thesis struggle... honestly, it is a total grind. I have been using these types of technical shortcuts for years and while the database-backed tools are definitely better than raw LLMs, you still gotta be super cautious. Even when a platform claims to link directly to the source text, make sure to manually verify the specific data in the results section. I have seen these tools misinterpret p-values or confidence intervals because they dont always grasp the full context of the experimental design. It is easy to get lazy when you are drowning in PDFs, but blindly trusting a summary is a quick way to tank your methodology chapter. Just double-check the limitations yourself tho, because AI tends to gloss over the nuanced stuff that actually matters for a solid defense.

🙌