The Valley of Meh
The most striking blind spot in LLM engineering is what John and Albert call the Valley of Meh - a trough in the prompt where LLM responsiveness dims like a firefly's glow in the night.
It mirrors how we experience movies: the opening scene burns bright in memory, the ending lingers clear, but the middle dissolves into a blur of indistinct moments.
This same pattern emerges in LLMs. You craft your prompt with careful context, whether for an application or a single task, yet the model seems to glide past crucial points. Essential context vanishes into the void.
This phenomenon intrigued me, but I hadn't pursued a solution until reading "Prompt Engineering for LLMs." The authors captured it perfectly as "The Valley of Meh" - an elegant name for an elusive problem.
Their solution reveals elegant simplicity: structure your prompts in chunks - introduction, first half, second half, conclusion. For longer prompts, expand to quarters or beyond. This chunking helps place vital elements - key context, sources, action steps - where they'll have the most impact.
The crucial insight? Never place critical context in that second quarter - the heart of the Valley of Meh.
Let's look at what belongs in each focused section:
First Quarter (High Focus):
Clear role definition ("You are a senior market analyst specializing in emerging tech")
Core context ("Analyzing Q4 2024 market data for AI startups")
Key constraints ("Focus only on Series A companies with >$10M revenue")
Critical parameters ("Consider market size, growth rate, and competitive landscape")
Last Quarter (High Focus):
Specific deliverables ("Create a bulleted summary with top 3 trends")
Output format ("Use markdown tables for financial metrics")
Quality criteria ("Each trend must include market size and growth rate")
Success metrics ("Recommendations should be actionable for Series A startups")
Here's a real example transforming a vague prompt:
Extensive testing confirms that concentrating key information in the first and final quarters yields consistently superior results. The approach first anchors the model's focus, then reinforces it with a deliberate return to the initial framework.
Picture a sandwich - the bread carries the pronounced flavors while the middle provides subtle support. Similarly, your prompt's outer sections command attention while the middle allows space for the model's natural reasoning to unfold.
The final chunk should crystallize action and eliminate ambiguity. Here, directness serves best - clear questions or explicit statements of expectation.
This solution to the Valley of Meh transcends mere technique - it fundamentally transforms prompt effectiveness. Integrate this approach, and watch your outputs gain precision and impact.
Like a firefly's signal in the darkness, your prompts will now know exactly when to shine.