Is Genai Living Up To Its Expectations Insights From 3 Years In The Trenches

Is GenAI Living Up to Its Expectations? Insights from 3 Years in the Trenches | AI21

Skip to Main Menu

Skip to Main Content

Skip to Footer

Back to Blog

-->

Back to Blog

Remember when everyone thought GenAI would solve… well, everything? Yeah, about that. Three years into the GenAI revolution, we’ve learned some hard truths about what these models can (and can’t) do. Let’s cut through the hype and talk about where we really stand—because the next wave of AI innovation, with true agentic capabilities, is closer than you think.

The Not-So-Simple Present

Here’s the thing about enterprise GenAI: it’s like trying to build a race car while driving it. Sure, the technology is incredible, but scaling it? That’s where things get spicy. Initial implementations have revealed challenges we didn’t anticipate—from infrastructure costs running 40-60% higher than projected to integration timelines stretching 2-3x longer than planned.

At AI21 Labs, we’ve developed Jamba, a hybrid model combining the efficiency of Mamba’s state-space layers with Transformer attention’s precision. This architecture enables processing up to 256k tokens of context, redefining enterprise applications like financial document synthesis and long-form data analysis. However, it’s important to note that no single architecture solves all challenges—each comes with its own tradeoffs.

Jamba represents a pivotal shift in GenAI—one driven by hard-won lessons and deliberate design choices.

What Actually Works Right Now (And What Doesn’t)

The secret sauce for successful, accurate GenAI output isn’t one ingredient—it’s a recipe of multiple complementary approaches:

1. RAG (Retrieval-Augmented Generation)

‍Think of it as giving your AI an open-book test. By providing relevant information at inference time, RAG enables models to answer complex questions with greater precision. The more relevant context the model has, the better the output.

However, RAG implementations often struggle with:

Data quality issues leading to inconsistent results

High computational costs for large-scale retrieval

Complex integration requirements with existing databases

2. Systematic workflows

‍Breaking big problems into bite-sized chunks, early “agentic” workflows relied on rigid, rule-based “static chains” to ensure accuracy. AI21’s conversational RAG engine exemplifies this approach by enhancing output quality through step-by-step task construction.

In building the RAG engine, but we’ve learned some hard lessons:

Early “agentic” workflows were too rigid and brittle

Error handling needs to be more sophisticated than initially thought

Human oversight remains crucial for complex tasks

3. Prompt engineering

‍ This fancy term describes the art of “talking” to a model effectively, teaching your AI to speak your language. It’s a bit like being a model whisperer. Providing tailored prompts—and sometimes “few-shot” examples—can significantly boost performance.

While prompt engineering can significantly boost performance, it’s not a silver bullet:

Results vary widely across different models

Maintaining prompt libraries becomes increasingly complex

Regular updates are needed as models evolve

4. Long-context processing

‍ Because sometimes you need the whole story. Why does long context matter? Databricks’ research showed that as context increases, so does accuracy. For real-world tasks, longer context windows are non-negotiable.

Longer context windows improve accuracy, but come with tradeoffs:

Processing costs increase substantially

Memory requirements can be prohibitive

Not all tasks benefit equally from extended context

Long context performance on 4 curated RAG datasets—Databricks

Combine these strategies above—past examples, a system approach for self-validation, and comprehensive context—and you’re feeding the model a mountain of data. Think about processing 10 years of Google, Meta, Amazon, and Microsoft’s 10-Ks. That’s 16,000 pages of context! Long context isn’t just helpful—it’s game-changing.

The Problem with Transformers

But here’s the catch: Transformer architectures dominate the GenAI market (e.g., GPT, Claude), but they hit a wall when processing very long contexts. Transformers are memory-hungry and painfully slow with large input sizes. Even models claiming to support long contexts rarely exceed 100k tokens in practice.

Those of you that played around with transformer-based models would also probably notice that even though some models have a declared long context window, they don’t really digest more than 100k tokens.

Enter Mamba , a state-space model and the first real alternative to Transformers. While more efficient, Mamba doesn’t quite match Transformer-level accuracy… which is where Jamba comes in.

Jamba: The Best of Both Worlds

Jamba stands for Joint Attention and Mamba . It’s our novel architecture that combines:

Transformer Attention Layers  for unparalleled accuracy.

Mamba Layers  for efficiency and throughput.

Each Jamba “block” includes seven Mamba layers and one attention layer—striking the perfect balance between quality and scalability. You’ll see below that in NVIDIA’s RULER benchmark, which measures effective context length, Jamba set a new standard. Our models excel up to 256k tokens, far beyond competitors.

Why this matters: With long-context capabilities, Jamba empowers use cases like:

Parsing financial reports across thousands of pages.

Synthesizing historical data for advanced insights.

The Long Context Benchmark: NVIDIA’s RULER

NVIDIA’s RULER benchmark tests how well models handle long contexts. It replaces older evaluations like “needle in a haystack,” which focused on spotting random strings in vast text but didn’t measure meaningful comprehension of large contexts.

RULER evaluates the effective context length of models—how much data they can actually process and use. While the benchmark stops at 128k tokens, our internal tests show that Jamba is the only model capable of reaching 256k tokens.

The GitHub leaderboard says it all: Jamba Large holds the top spot, with Jamba not far behind in third.

Critical Lessons from the Field

What We Got Wrong

Initial Cost Projections – Infrastructure costs typically exceeded estimates by 40-60%

Implementation Timelines – Depending on a company’s data science maturity, projects sometimes took 2-3x longer...